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  • A Novel Finite-Time Adaptive Fuzzy Tracking Control Scheme for Nonstrict Feedback Systems
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 
    Yang Liu; Xiaoping Liu; Yuanwei Jing; Ziye Zhang

    This work investigates a finite-time adaptive fuzzy tracking control problem for a class of non-strict feedback nonlinear systems from a new point of view. A new concept, named Finite-Time Performance Function (FTPF), is defined in this paper for the first time. Moreover, a finite-time adaptive state feedback fuzzy tracking controller is derived based on fuzzy approximation, backstepping technique and Prescribed Performance Control (PPC), which guarantees that all the signals of the closed-loop system are bounded, the output tracking error converges to a prescribed arbitrarily small region within a finite-time interval, and maximum overshoot is not more than a predefined level. In addition, a controller design process is given, which is less complex than the existing finite-time control design methods. Three simulation studies are provided to verify the feasibility and effectiveness of the theoretical finding in this study.

    更新日期:2018-08-20
  • A SURVEY OF FUZZY MIN MAX NEURAL NETWORKS FOR PATTERN CLASSIFICATION: VARIANTS AND APPLICATIONS
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-08-17
    Osama Nayel Sayaydeh; Mohammed Falah Mohammed; Chee Peng Lim

    Over the last few decades, pattern classification has become one of the most important fields of artificial intelligence because it constitutes an essential component in many real-world applications. Artificial neural networks and fuzzy logic are the two most widely used models in pattern classification. To build an efficient and powerful model, researchers have introduced hybrid models that combine both fuzzy logic and artificial neural networks. Among the existing hybrid models, the family of Fuzzy Min-Max (FMM) neural networks offers a premier model for undertaking pattern classification problems. While the original FMM model is useful in terms of its capability of online learning, it suffers from several limitations in its learning procedure. Therefore, researchers have proposed numerous improvements to overcome the limitations over the years. In this paper, we conduct a comprehensive survey on the developments of FMM-based models for pattern classification. To allow researchers in selecting the most suitable FMM variants and to provide a proper guideline for future developments, this study divides the FMM variants into two main categories, namely FMM variants with and without contraction. This division facilitates understanding of the improvements on the original FMM model, as well as enables identification of the limitations that still exist in various FMM-based models. We also summarize the use of FMM and its variants in solving different benchmark and real-world pattern classification problems. In addition, future trends and research directions of FMM-based models are highlighted.

    更新日期:2018-08-18
  • The Properties of Fuzzy Tensor and Its Application in Multiple Attribute Group Decision Making
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-08-17
    Shengyue Deng; Jianzhou Liu; Xinfan Wang

    The properties of fuzzy tensor and the product of fuzzy tensor and fuzzy vector are studied in the paper. The concept of fuzzy tensor is defined through expanding the fuzzy matrix. The product of fuzzy tensor and fuzzy vector is proved to be a fuzzy linear transformation. The general form of the fuzzy synthetic evaluation model is established to solve multiple attribute group decision making problems. Finally, two numerical examples of multiple attribute group decision making are provided to demonstrate the feasibility and efficiency of the fuzzy synthetic evaluation.

    更新日期:2018-08-18
  • An additive consistency and consensus-based approach for uncertain group decision making with linguistic preference relations
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 
    Jing-Feng Tian; Zhiming Zhang; Ming-Hu Ha

    Linguistic preference relations (LPRs) can indicate the decision makers (DMs)' qualitative pairwise judgments regarding a set of alternatives in uncertain multi-criteria decision making (UMCDM) problems. This paper aims to formulate several goal programming models for managing the additive consistency and consensus of LPRs and then develop an additive consistency and consensus-based method for group decision making (GDM) with LPRs. First, this paper offers a consistency index to quantify the consistency level for LPRs and define acceptably consistent LPRs. For an LPR that is unacceptably additive consistent, several additive consistency-based programming models are developed to address the inconsistency and to establish an acceptably consistent LPR. Then, an additive consistency-based procedure to generate the priority weight vector from the LPR is offered. An additive consistency-based algorithm for decision making with an LPR is presented. Subsequently, considering the consensus in GDM, a consensus index is proposed for gauging the agreement degree among individual LPRs. Regarding individual LPRs that do not exhibit acceptably additive consistency or acceptable consensus, several goal programming models to derive new LPRs with acceptable consistency and consensus are provided. Afterward, the DMs' weights are determined objectively and individual LPRs are integrated into a collective LPR. An additive consistency and consensus-based GDM method with a group of LPRs is developed. Finally, two practical numerical examples are offered and a comparative analysis is presented.

    更新日期:2018-08-13
  • A Comment on "A Direct Approach for Determining the Switch Points in the Karnik-Mendel Algorithm"
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 
    Chao Chen; Dongrui Wu; Jonathan Mark Garibaldi; Robert John; Jamie Twycross; Jerry M. Mendel

    This letter is a supplement to the previous paper "A Direct Approach for Determining the Switch Points in the Karnik-Mendel Algorithm". In the previous paper, the enhanced iterative algorithm with stop condition (EIASC) was shown to be the most inefficient in R. Such outcome is apparently different from the results in another paper in which EIASC was illustrated to be the most efficient in Matlab. An investigation has been made into this apparent inconsistency and it can be confirmed that both the results in R and Matlab are valid for the EIASC algorithm. The main reason for such phenomenon is the efficiency difference of loop operations in R and Matlab. It should be noted that the efficiency of an algorithm is closely related to its implementation in practice. In this letter, we update the comparisons of the three algorithms in the previous paper based on optimised implementations under five programming languages (Matlab, R, Python, C and Java). From this, we conclude that results in one programming language cannot be simply extended to all languages.

    更新日期:2018-08-13
  • Finite-time Convergence Adaptive Fuzzy Control for Dual-Arm Robot with Unknown Kinematics and Dynamics
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-08-10
    Chenguang Yang; Yiming Jiang; Jing Na; Zhijun Li; Long Cheng; Chunyi Su

    Control design for robots with kinematic and dynamic uncertainties has been well studied for single arm cases, but little effort has been made to dual-arm cases. Due to the strongly coupled nonlinearity of the dual-arm robot and the internal forces generated by the grasping common object, the dual-arm robot control with uncertain kinematics and dynamics becomes extremely challenging. In this paper, we developed an adaptive fuzzy control scheme for a dual arm robot in the presence of both kinematic and dynamic uncertainties. The approximate Jacobian matrix (AJM) was applied to address the uncertain kinematic control, while decentralized fuzzy logic controller (FLC) was constructed to compensate for uncertain dynamics of the robotic arms and the manipulated objects. In addition, to guarantee the finite time convergence of the estimated parameters, a novel finite-time convergence parameter adaptation (FCPA) technique was developed for the estimation of kinematic parameters and fuzzy logic weights. The estimated parameters were guaranteed to converge to small neighborhoods around their ideal values in a finite time, which enables the designer to re-use these learned weight values next time without relearning. In addition, a partial persistent excitation (PPE) property of Gaussian membership based fuzzy basis function (GFBF) was established, such that the condition of the conventional persistent excitation (PE) is relaxed. Extensive simulation studies have been carried out based on a dual-arm robot to illustrate the effectiveness and efficiency of the proposed approach.

    更新日期:2018-08-11
  • Sparse Representation-Based Intuitionistic Fuzzy Clustering Approach to Find the Group Intra-Relations and Group Leaders for Large-Scale Decision Making
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-08-09
    Ruxi Ding; Xueqing Wang; Kun Shang; Bingsheng Liu; Francisco Herrera

    In this paper, a sparse representation-based intuitionistic fuzzy clustering (SRIFC) approach is presented for solving the large-scale decision making (LSDM) problem. It consists of two algorithms: the sparse representation-based intuitionistic fuzzy clustering-exactly precision (SRIFC-E) algorithm (which is presented for an exactly precision requirement), and the sparse representation-based intuitionistic fuzzy clustering-soft precision and scalable (SRIFC-S) algorithm (which is proposed for soft precision and scalable requirements). In the proposed SRIFC approach, decision makers are clustered into several interest groups according to their interest preferences and relation sparsity in their intuitionistic fuzzy assessment information. The main contribution of the presented SRIFC approach is to investigate the group intra-relations among DMs and to detect the group leaders for each interest group during the clustering process. According to the illustrative experiment results, the presented SRIFC approach is an adaptive and unsupervised clustering method and presents more robust and efficient for LSDM problems.

    更新日期:2018-08-10
  • Wavelet-TSK-type Fuzzy Cerebellar Model Neural Network for Uncertain Nonlinear Systems
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 
    Jing Zhao; Chih-Min Lin

    This paper presents a novel fuzzy neural network structure for uncertain nonlinear systems based on the framework of cerebellar model neural network (CMNN) and the Takagi -Sugeno-Kang (TSK) fuzzy knowledge base. To solve the uncertainty problem of nonlinear systems better, the wavelet functions are used in the consequent parts of each TSK fuzzy rule instead of the linear combination of the input variables, and the Gaussian-type functions are used as the membership functions in the antecedent parts. Combining the advantages of the wavelet function, the CMNN and the TSK fuzzy system, this new model is more suitable for uncertain nonlinear systems. To provide rapid training, parameter update rules of the proposed model are derived based on the gradient descent method, in which the learning rates are online adapted. Furthermore the Lyapunov function is used to analyze the convergence of the considered systems. Finally, four different types of applications are applied to demonstrate the performance of the proposed model. The comparison simulation results with other models verify the effectiveness of this new model.

    更新日期:2018-08-06
  • Novel Stabilization Criteria for T-S Fuzzy Systems with Affine Matched Membership Functions
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 
    Sangmoon Lee

    This paper presents a new parallel distributed compensation controller design approach for T-S fuzzy control systems with affine matched membership functions in the system and controller. In the new fuzzy control, affine transformed membership functions are adopted by scaling and biasing the original membership functions of the system. Stabilization and performance criterion of the closed-loop T-S fuzzy systems are obtained through a new parametrized linear matrix inequality which is rearranged by affine matched membership functions. The conservativeness of stabilization condition for the T-S fuzzy system is significantly relaxed by utilizing the constraints condition of the controller's membership functions which is determined from the difference of each transformed membership functions. In addition, the controller gain is reconstructed by a decision variable separation technique with two different free weighting matrices without any scaling parameter. The superiority of proposed method is verified through numerical examples.

    更新日期:2018-08-06
  • A New Possibilistic Optimization Model for Multiple Criteria Assignment Problem
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-11
    Mukesh Kumar Mehlawat; Pankaj Gupta; Witold Pedrycz

    This paper presents a new multiple criteria optimization model of an assignment problem with imprecise coefficients. Besides, minimizing the total cost, total time of finishing jobs, and maximization of the overall achieved quality, we introduce a new criterion that minimizes the number of workers employed to finish all jobs. It contributes significantly in multi-job assignment to adjust the number of workers assigned to at least one job for balancing work allocation among the workers. Furthermore, we employ new diversification constraints to obtain a reasonable tradeoff between the number of workers employed and number of jobs assigned. A new interactive possibilistic programming approach is developed for trapezoidal possibility distributions, which uses$\alpha$-level sets to incorporate confidence levels of the decision maker in his fuzzy judgments leading to$\alpha$-efficient solutions. Numerical experiments are conducted using data coming from a manpower planning problem to demonstrate working of the proposed multiple criteria assignment model and effectiveness of the fuzzy interactive approach.

    更新日期:2018-08-02
  • Fuzzy Evaluations of Image Segmentations
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-13
    Bartosz Ziółko; David Emms; Mariusz Ziółko

    Evaluation measures for images segmentation are suggested. The methods compare the results of automatic segmentation with ground truth. The presented methods for assessing the similarity of the segments are based on three different approaches: the number of pixels in common, the similarity of the contours, and the location of centroids. The fuzzy approach consists of considering the significance of segment differences in relation to the size of the segments. The final measures for the whole images are based on recall and precision, widely used in information retrieval tasks. The approaches presented in this paper apply the fuzzy set theory instead of classical evaluation methods.

    更新日期:2018-08-02
  • A Fault Detection Approach for Nonlinear Systems Based on Data-Driven Realizations of Fuzzy Kernel Representations
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-13
    Linlin Li; Steven X. Ding; Ying Yang; Kaixiang Peng; Jianbin Qiu

    This paper is devoted to the data-driven fault detection of nonlinear systems. For our purpose, the definition of Takagi–Sugeno fuzzy data-driven forms of kernel representations for nonlinear systems is introduced first, which builds the basis of our work. The major contributions consist of two parts. In the first part, a data-driven method for fuzzy process modeling is proposed, and associated with it, some modeling issues are addressed with the aid of the so-called randomized algorithm technique in the probabilistic framework. It is followed by a data-driven realization of fuzzy kernel representation and its implementation in the fault detection system design. To link the data-driven methods to the well-established observer-based fault detection approaches, the recursive form of the fuzzy kernel representation is proposed. In the second part, the fuzzy-observer-based fault detection design scheme is investigated based on the recursive fuzzy kernel representation. The main results of our study are illustrated by an experimental study on the laboratory setup of a three-tank system.

    更新日期:2018-08-02
  • Dual-Loop Self-Learning Fuzzy Control for AMT Gear Engagement: Design and Experiment
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-12-01
    Xiangyu Wang; Liang Li; Kai He; Congzhi Liu

    Gear engagement is the most important part in gear-shift process of automated manual transmission (AMT). However, it is practical to encounter complicated nonlinearities, uncertainties, and multistage characteristics in the system model, so the controller design for the AMT gear engagement becomes challenging. This paper proposes a dual-loop self-learning fuzzy control framework. In the outer loop, the self-learning rules based on fuzzy logic is designed to adjust desired trajectory of actuator motor. In the inner loop, the gear engagement is divided into three stages, and a fuzzy controller with model reference self-learning algorithm is designed, which controls the actuator motor to track the desired trajectory. Besides, the control parameters could be adjusted to be optimal automatically when the parameters change. Results of simulations and experiments indicate that the proposed method is able to realize the smooth and fast control of gear engagement. In addition, the self-learning fuzzy controller can be extended to deal with other nonlinear systems with uncertain and even unknown parameters.

    更新日期:2018-08-02
  • Robustifying OWA Operators for Aggregating Data With Outliers
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-15
    Gleb Beliakov; Simon James; Tim Wilkin; Tomasa Calvo

    We propose a version of ordered weighted averaging (OWA) operators, which are robust against inputs with outliers. Outliers may heavily bias the outputs of the standard OWA. The penalty-based method proposed here comprises both outlier detection and reallocation of weights of the OWA. At the first stage, the outliers are identified based on a robust criterion that can accommodate up to half the inputs being outliers, but at the same time not removing the inputs unnecessarily. Three numerical algorithms for calculating the optimal value of this criterion are proposed. At the second stage, the OWA weights are recalculated for a subset of clean data while preserving the overall character of the weighting vector. The method is numerically tested on simulated data and exemplified on aggregating a large number of online ratings where the outliers represent biased, missing, or erroneous evaluations.

    更新日期:2018-08-02
  • GA-Based Fuzzy Energy Management System for FC/SC-Powered HEV Considering H2Consumption and Load Variation
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-12-04
    Ridong Zhang; Jili Tao

    The combination of the fuel cell (FC) and supercapacitor for a hybrid electric vehicle (HEV) has the benefit of compensating for the slow dynamic response and avoiding reactant starvation of FC. Energy management system (EMS) is critical to HEV and a fuzzy controller plus low-pass filter is proposed to prolong the FC lifetime and decrease the hydrogen consumption. The constrained biobjective optimization problem for fuzzy EMS is then solved by an improved genetic algorithm (GA), where the decimal and rule base encoding, constraint handling, the pruning and maintain operator are designed to optimize both the fuzzy rule base and the parameters of the membership functions. Simulation results of highway fuel economy certification test, urban dynamometer driving schedule, and new European drive cycle illustrate that the proposed approach can smooth the output of FC with robustness and be implemented in real time, which decreases 19% current variation with about 10% increase of H2consumption.

    更新日期:2018-08-02
  • Fuzzy Control With Guaranteed Cost for Nonlinear Coupled Parabolic PDE-ODE Systems via PDE Static Output Feedback and ODE State Feedback
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-18
    Huan-Yu Zhu; Huai-Ning Wu; Jun-Wei Wang

    This paper investigates the guaranteed cost fuzzy control (GCFC) problem for a class of nonlinear systems modeled by an$n$-dimension ordinary differential equation (ODE) coupled with a semilinear scalar parabolic partial differential equation (PDE). A Takagi–Sugeno (T–S) fuzzy coupled parabolic PDE-ODE model is initially proposed to accurately represent the nonlinear coupled system. Then, on the basis of the T–S fuzzy coupled model, a GCFC design is developed in terms of linear matrix inequalities to exponentially stabilize the coupled system while providing an upper bound for a prescribed quadratic cost function. The proposed fuzzy control scheme consists of the ODE state feedback and the PDE static output feedback employing locally collocated piecewise uniform actuators and sensors. Moreover, a suboptimal GCFC problem is also addressed to minimize the cost bound. Finally, the developed method is applied to the cruise control and surface temperature cooling of a hypersonic rocket car.

    更新日期:2018-08-02
  • Incremental Rule Splitting in Generalized Evolving Fuzzy Systems for Autonomous Drift Compensation
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-18
    Edwin Lughofer; Mahardhika Pratama; Igor Skrjanc

    Gradual drifts in data streams are usually hard to detect and often do not necessarily trigger the evolution of new fuzzy rules during model adaptation steps in order to represent the new, drifted data distribution(s) appropriately in the fuzzy model. Over time, they thus lead to oversized rules with untypically large local errors (typically also worsening the global model error), as representing joint local data distributions before and after a drift happened likewise. We therefore propose anincremental rule splitting conceptforgeneralized fuzzy rulesin order to autonomously compensate these negative effects of gradual drifts. Our splitting condition is based on the local error of rules measured in terms of a weighted contribution to the whole model error and on the size of the rules measured in terms of the volume of the associated clusters. We use the concept of statistical process control in order to omit an extra threshold parameter in our splitting condition. The splitting technique relies on the eigendecomposition of the rule covariance matrix to adequately manipulate the largest eigenvector and eigenvalues in order to retrieve the new centers and contours of the two split rules. Furthermore, we guarantee sufficient flexibility in adapting the shapes and consequents of the split rules to the new drifted situation in the stream by integrating a specificdynamic and smooth forgettingconcept of older samples, which formed the original (nonsplit) rules.Robustnessagainst outliers is guaranteed by the realization of atwo-layer model building process, where one layer represents the cluster partition and the other layer the rule partition: Only clusters becoming significant over time are accepted as rules in the fuzzy model. The splitting concepts are integrated in the generalized smart evolving learning engine for fuzzy systems (termed asGen-Smart-EFS) and successfully tested on two real-world application scenarios, engine test benches and rolling mills, the latter including a real-occurring gradual drift (whose position in the data is known). Results show clearly improved error trend lines over time when splitting is applied, compared to the case when it is not applied: reduction of the mean absolute model error by about one third (rolling mills) and about one half (engine test benches).

    更新日期:2018-08-02
  • Asynchronous Filtering of Nonlinear Markov Jump Systems With Randomly Occurred Quantization via T–S Fuzzy Models
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-20
    Jie Tao; Renquan Lu; Hongye Su; Peng Shi; Zheng-Guang Wu

    This paper focuses on the dissipativity-based asynchronous filtering problem for a class of discrete-time Takagi–Sugeno fuzzy Markov jump systems subject to randomly occurred quantization. Considering the random fluctuations of network conditions, the randomly occurred quantization is introduced to describe the quantization phenomenon appearing in a probabilistic way. To take full advantage of the partial information of system modes for the desired system performance, we adopt the asynchronous filter in which mode transition matrix is nonhomogeneous. The mode-dependent time-varying delays are introduced, which have different bounds for different system modes. Via fuzzy-mode-dependent Lyapunov functional approach that can reduce conservatism, a sufficient condition on the existence of the asynchronous filter is derived such that the filtering error system is stochastically stable and strictly$(\mathcal{Q}, \mathcal{S},\mathcal{R})$-dissipative. Then, the gains of the filter are obtained by solving a set of linear matrix inequalities (LMIs). An example is utilized to illustrate the validity of the developed filter design technique where the relationships among optimal dissipative performance indices, delays, quantization parameter, and the degree of asynchronous jumps are given.

    更新日期:2018-08-02
  • Dynamic Fuzzy Rule Interpolation and Its Application to Intrusion Detection
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-12-04
    Nitin Naik; Ren Diao; Qiang Shen

    Fuzzy rule interpolation (FRI) offers an effective approach for making inference possible in sparse rule-based systems (and also for reducing the complexity of fuzzy models). However, requirements of fuzzy systems may change over time and hence, the use of a static rule base may affect the accuracy of FRI applications. Fortunately, an FRI system in action will produce interpolated rules in abundance during the interpolative reasoning process. While such interpolated results are discarded in existing FRI systems, they can be utilized to facilitate the development of a dynamic rule base in supporting subsequent inference. This is because the otherwise relinquished interpolated rules may contain possibly valuable information, covering regions that were uncovered by the original sparse rule base. This paper presents a dynamic fuzzy rule interpolation (D-FRI) approach by exploiting such interpolated rules in order to improve the overall system's coverage and efficacy. The resulting D-FRI system is able to select, combine, and generalize informative, frequently used interpolated rules for merging with the existing rule base while performing interpolative reasoning. Systematic experimental investigations demonstrate that D-FRI outperforms conventional FRI techniques, with increased accuracy and robustness. Furthermore, D-FRI is herein applied for network security analysis, in devising a dynamic intrusion detection system (IDS) through integration with the Snort software, one of the most popular open source IDSs. This integration, denoted as D-FRI-Snort hereafter, delivers an extra amount of intelligence to predict the level of potential threats. Experimental results show that with the inclusion of a dynamic rule base, by generalising newly interpolated rules based on the current network traffic conditions, D-FRI-Snort helps reduce both false positives and false negatives in intrusion detection.

    更新日期:2018-08-02
  • A Dynamic Weight Determination Approach Based on the Intuitionistic Fuzzy Bayesian Network and Its Application to Emergency Decision Making
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-20
    Zhinan Hao; Zeshui Xu; Hua Zhao; Hamido Fujita

    The weight information has been playing a key role in information fusion and dynamic decision making process. Most existing methods for determining weights under dynamic environments only derive the period weights by using the distribution functions of time series, but there is little investigation of the determination of dynamic attribute weights over time. To solve this issue, we first develop an intuitionistic fuzzy Bayesian network to obtain the practical attribute weights under uncertain environment. Then, we propose a conceptual framework for dynamic intuitionistic fuzzy decision making, and based on which, we develop a dynamic decision making approach integrating the prospect theory to solve the risk decision making problems. Furthermore, a case study involving the mine emergency decision making problem is presented to illustrate the application of our approach. Finally, we discuss the characteristics and limitations of our approach in detail.

    更新日期:2018-08-02
  • Data-Driven Compression and Efficient Learning of the Choquet Integral
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-20
    Muhammad Aminul Islam; Derek T. Anderson; Anthony J. Pinar; Timothy C. Havens

    The Choquet integral (ChI) is a parametric nonlinear aggregation function defined with respect to the fuzzy measure (FM). To date, application of the ChI has sadly been restricted to problems with relatively few numbers of inputs; primarily as the FM has$2^N$variables for$N$inputs and$N(2^{N-1}-1)$monotonicity constraints. In return, the community has turned to density-based imputation (e.g., Sugeno$\lambda$-FM) or the number ofinteractions(FM variables) are restricted (e.g.,$k$-additivity). Herein, we propose a new scalable data-driven way to represent and learn the ChI, making learning computationally manageable for larger$N$. First, data supported variables are identified and used in optimization. Identification of these variables also allows us recognize future ill-posed fusion scenarios; ChIs involving variable subsets not supported by data. Second, we outline an imputation function framework to address data unsupported variables. Third, we present a lossless way to compress redundant variables and associated monotonicity constraints. Finally, we outline a lossy approximation method to further compress the ChI (if/when desired). Computational complexity analysis and experiments conducted on synthetic datasets with known FMs demonstrate the effectiveness and efficiency of the proposed theory.

    更新日期:2018-08-02
  • Consensus Building Process in Group Decision Making—An Adaptive Procedure Based on Group Dynamics
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-21
    Mahima Gupta

    In group decision making (GDM) problems, it is critical to arrive at an acceptable level of consensus in a group for a stable and implementable decision. This process is carried out by a moderator (either real or virtual) who advises the members to change their opinions in order to achieve higher consensus in the group. In this paper, we present an algorithm that maps the consensus evolution process of GDM based on interrelationships of members in the group. The members’ views are taken in linguistic form to incorporate qualitative aspects and vagueness, if any, in their judgment. The novelty of this work lies in the computation of shift in members’ opinions depending on their level of adoption of other ideas and support of their ideas in the group. The algorithm accounts for the relations among the members based on their earlier interactions in a network and support of their views contextual to a specific problem. The conditions governing the impact of level of adoption and support in the group are explicated using fuzzy if–then rules. The proposed algorithm converges after a finite number of iterations and maintains consistency in the members’ opinions throughout the process.

    更新日期:2018-08-02
  • Adaptive Fuzzy Prescribed Performance Control for Nonlinear Switched Time-Delay Systems With Unmodeled Dynamics
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-22
    Changchun Hua; Guopin Liu; Liang Li; Xinping Guan

    This paper considers the adaptive fuzzy output feedback tracking control problem for a class of uncertain nonlinear switched systems with time delay and unmodeled dynamics. Based on a kind of switched K-filters, a prescribed performance control scheme is proposed to guarantee the tracking performance and restrain the fluctuation caused by switches between submodes as well. In addition, fuzzy logic systems (FLSs) are use to approximate unknown nonlinear functions and dynamic surface control (DSC) method is employed to eliminate the explosion of complexity problem inherent in traditional backstepping method. The proposed controllers of corresponding subsystems guarantee that all closed-loop signals remain bounded under a class of switching signals with average dwell time (ADT). A numerical simulation is performed to illustrate the effectiveness of the proposed approach.

    更新日期:2018-08-02
  • Symmetry Information Based Fuzzy Clustering for Infrared Pedestrian Segmentation
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-26
    Xiangzhi Bai; Yingfan Wang; Haonan Liu; Sheng Guo

    Pedestrian detection in infrared images is always a challenging task. Segmentation is an important step of pedestrian detection. An accurate segmentation could provide more information for further analysis. In this paper, an improved Fuzzy C-Means clustering method, which incorporates geometric symmetry information, is proposed for infrared pedestrian segmentation. In the proposed method, symmetry information is introduced by Markov random field theory. Moreover, a new metric is utilized to handle the weak symmetry of pedestrian. In addition, a whole procedure is proposed to extract infrared pedestrians. The experimental results indicate that our method performs better for infrared pedestrian segmentation and obtains better segmentation results compared with other state-of-the-art methods.

    更新日期:2018-08-02
  • Cognitive Integrals With Its Generalized and Adapted Forms
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-12-04
    Lesheng Jin; Radko Mesiar

    Motivated by some cognition styles, this study first proposes a new type of preaggregation functions called cognitive integrals with its generalized forms. Rather than being affected only by selected capacity, generalized cognitive integrals consider other two parameters, the cognitive strength and the involved semicopula. Our main focus is on cognitive strength, by which the integrals values will be monotonically decreasing with respect to our cognitive strength. After some appropriated adaptations, we later propose the concept of adapted cognitive integrals with two equivalent forms of itself. Not only being a type of the preaggregation functions, we prove that adapted cognitive integrals are indeed one new type of aggregation functions, still equipped with the two new parameters like in cognitive integrals.

    更新日期:2018-08-02
  • Fuzzy Entropy and Its Application for Enhanced Subspace Filtering
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-26
    Hong-Bo Xie; Bellie Sivakumar; Tjeerd W. Boonstra; Kerrie Mengersen

    Fuzzy entropy (FuzzyEn), which employs the fuzzy probability to characterize the similarity between vectors, is a robust nonlinear statistic to quantify the complexity or regularity of nonlinear time series. The aim of this study is to investigate the statistical properties of FuzzyEn and improve the subspace denoising technique using FuzzyEn. We first show the asymptotic normality of FuzzyEn and derive its variance for finite sample behavior. We then analyze the two pending and fundamental issues in subspace denoising, i.e., depending on the so-called “noise floor” and the unaltered noise existing in signal subspace, from the point of view of fuzzy logic. A FuzzyEn-assisted subspace iterative soft threshold (FESIST) denoising method, which can effectively overcome the deficiency in the existing subspace filtering (SSF) techniques, is presented. The effectiveness of the method is first demonstrated on two synthetic chaotic series and then tested on real biological signals. The results demonstrate the superiority of the proposed method over existing SSF techniques, as well as the empirical mode decomposition and wavelet decomposition approaches.

    更新日期:2018-08-02
  • Characterization of a Class of Fuzzy Implication Functions Satisfying the Law of Importation With Respect to a Fixed Uninorm—Part I
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-26
    Sebastia Massanet; Daniel Ruiz-Aguilera; Joan Torrens

    The law of importation is an important property of fuzzy implication functions with interesting applications in approximate reasoning and image processing. This property has been extensively studied and some open problems have been proposed in the literature. In particular, in this paper, we partially solve an open problem related to this property posed some years ago. Specifically, given a fixed uninorm, all fuzzy implication functions that satisfy the law of importation with respect to this uninorm, and having an$\alpha$-section that is a continuous negation, are characterized. This characterization is specially detailed for the case of uninorms lying in each one of the most usual classes of uninorms. This is done in two different papers, this paper and the forthcoming paper (S. Massanet, D. Ruiz-Aguilera, and J. Torrens, “Characterization of a class of fuzzy implication functions satisfying the law of importation with a fixed uninorm—Part II,” IEEE Trans. Fuzzy Syst., to be published). In particular, in this paper the case of uninorms in$\mathcal {U}_{\min }$is solved, whereas the cases where the uninorm is in the other usual classes (that is, idempotent, representable, and continuous in the open unit square) are left for the above-mentioned paper.

    更新日期:2018-08-02
  • Characterization of a Class of Fuzzy Implication Functions Satisfying the Law of Importation With Respect to a Fixed Uninorm—Part II
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-26
    Sebastia Massanet; Daniel Ruiz-Aguilera; Joan Torrens

    The law of importation is an important property of fuzzy implication functions because of its interesting applications in approximate reasoning and image processing. In this paper, as a continuation of the paper [S. Massanet, D. Ruiz-Aguilera, and J. Torrens, “Characterization of a class of fuzzy implication functions satisfying the law of importation with respect to a fixed uninorm—Part I,”IEEE Trans. Fuzz Syst., to be published.], the characterization of all fuzzy implication functions that satisfy the law of importation with respect to a given uninorm$U$, and having an$\alpha$-section that is a continuous fuzzy negation is given for the cases when the uninorm$U$lies in one of the most used classes of uninorms. As the case when the uninorm$U$is in${\mathcal U}_{\min }$was already solved in [S. Massanet, D. Ruiz-Aguilera, and J. Torrens, “Characterization of a class of fuzzy implication functions satisfying the law of importation with respect to a fixed uninorm—Part I,”IEEE Trans. Fuzz Syst., to be published.], this paper focuses in the cases when$U$is an idempotent uninorm, a representable uninorm, or a uninorm continuous in the open unit square.

    更新日期:2018-08-02
  • Event-Triggered Fault Detector and Controller Coordinated Design of Fuzzy Systems
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-09-27
    Xiaojie Su; Fengqin Xia; Ligang Wu; C. L. Philip Chen

    This paper attempts to propose a new solution to the event-triggered fault detection problem for discrete-time Takagi–Sugeno fuzzy systems in a network environment. First, for the original Takagi–Sugeno fuzzy system, our focus is on constructing a new based-network residual system, considering the event-triggering mechanism, interval time-varying delays, and packet dropouts. Under the established system, a less conservative basis-dependent stability condition is obtained by using the reciprocally convex technique, which ensures that the corresponding residual system is mean-square asymptotically stable with a given$\mathcal{H}_{\infty }$performance. Second, the desired fuzzy-rule-dependent fault detector and the controller scheme are established using a variable substitution approach. Furthermore, these criteria can be transformed into convex optimization problems and then calculated by the standard optimization toolbox. Finally, the advantages of the proposed fault detector and controller coordinated design technique are illustrated by the simulation results.

    更新日期:2018-08-02
  • $\epsilon$-Bisimulation Relations for Fuzzy Automata
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-10-06
    Chao Yang; Yongming Li

    This paper investigates the approximate bisimulation relations for fuzzy automata in order to study the approximate minimization problem of fuzzy automata. For a small positive real number$\epsilon$, we introduce the notion of$\epsilon$-bisimulation relations between two fuzzy automata, and prove that the behavior of a fuzzy automaton$A$differs by$\epsilon$from the behavior of a fuzzy automaton$B$under a$\epsilon$-bisimulation relation between them. Also, the notion of surjective functional$\epsilon$-bisimulation relations between two fuzzy automata is defined. According to surjective functional$\epsilon$-bisimulation relations, we discuss$\epsilon$-bisimulation relations for a fuzzy automaton. A construction of aggregated fuzzy automaton by the given$\epsilon$-bisimulation for a fuzzy automaton is given. Furthermore, we find that there might not exist the greatest$\epsilon$-bisimulation relation for a fuzzy automaton, and we novelly give an effective algorithm to construct all maximal$\epsilon$-bisimulation relations for the given fuzzy automaton. Finally, we point out that bisimulation relations for a fuzzy automaton are also$\epsilon$-bisimulation relations, the conditions for the real number$\epsilon$to ensure the existence of the greatest$\epsilon$-bisimulation are also discussed.

    更新日期:2018-08-02
  • Interval Type-2 Fuzzy Logic Modeling and Control of a Mobile Two-Wheeled Inverted Pendulum
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-10-06
    Jian Huang; MyongHyok Ri; Dongrui Wu; Songhyok Ri

    This paper presents an integrated interval type-2 fuzzy logic approach that simultaneously models and controls an underactuated mobile two-wheeled inverted pendulum (MTWIP), which suffers from modeling uncertainties and external disturbances. The control objective is to attain the desired position and direction while keeping the MTWIP balanced. It is achieved by integrating four interval type-2 fuzzy logic systems (IT2 FLSs): the first IT2 FLS describes the dynamics of the MTWIP using a Takagi–Sugeno model, the second IT2 FLS controls the balance of the MTWIP using also a Takagi–Sugeno model, and the third and fourth IT2 FLSs control its position and direction, respectively, using a Mamdani model. A linear matrix inequality based design approach is also proposed to guarantee the stability of the balance controller. The proposed approach is compared with a type-1 FLS in real-world experiments. All results demonstrate that the IT2 FLS outperforms the type-1 FLS, especially under modeling uncertainties and external disturbances.

    更新日期:2018-08-02
  • Fuzzy Double Trace Norm Minimization for Recommendation Systems
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-10-06
    Fanhua Shang; Yuanyuan Liu; James Cheng; Da Yan

    Recovering low-rank matrices from incomplete observations is a fundamental problem with many applications, especially in recommender systems. In theory, under certain conditions, this problem can be solved by convex or nonconvex relaxation. However, most existing provable algorithms suffer from superlinear per-iteration cost, which severely limits their applicability to large-scale problems. In this paper, we propose a novel fuzzy double trace norm minimization (DTNM) method for recommender systems. We first present a tractable DTNM model, in which we can integrate both the user social relationship and the user reputation information using a fuzzy weighting way and coupling fuzzy matrix factorization. In essence, our model is a Schatten-${1/2}$quasi-norm minimization problem. Moreover, we develop two efficient augmented Lagrangian algorithms to solve the proposed problems, and prove the convergence of our algorithms. Finally, we investigate the empirical recoverability properties of our model and its advantage over classical trace norm. Extensive experimental results on both synthetic and real-world data sets verified both the efficiency and effectiveness of our method compared with the state-of-the-art algorithms.

    更新日期:2018-08-02
  • Fuzzy Logic Aided Fault-Tolerant Control Applied to Transport Aircraft Subject to Actuator Stuck Failures
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-10-06
    Xiang Yu; Yu Fu; Xiaoyan Peng

    Failure to counteract in-flight actuator stuck failures within a limited amount of time can result in catastrophic consequences. Aircraft safety is also undermined when fault-tolerant control (FTC) systems are designed based on inaccurate diagnostic results. This paper presents an FTC scheme for transport aircraft subject to actuator stuck failures, which is integrated with fuzzy logic system (FLS), real finite-time sliding mode control (RFTSMC), and least squares control allocation (LSCA) techniques. First, the term, including the stuck information, is obtained by resorting to an FLS. The norm of the weight matrix is adopted in the FLS estimation, such that the amount of the adaptive parameters is independent of the number of FLS rules. Following this, a virtual fuzzy logic aided FTC strategy is developed using RFTSMC, by which the actuator stuck failure is counteracted within finite time. Furthermore, an LSCA unit is capable of distributing the commands to the fault-free actuators, with explicit consideration of incorrectly identified values of actuator effectiveness indicators. Finally, simulation studies of a nonlinear Boeing 747 longitudinal model are performed to validate the effectiveness of the proposed scheme.

    更新日期:2018-08-02
  • Adaptive Fuzzy Fault-Tolerant Control of Nontriangular Structure Nonlinear Systems With Error Constraint
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-10-09
    Yongming Li; Zhiyao Ma; Shaocheng Tong

    In this paper, an adaptive fuzzy fault-tolerant control approach is proposed for a class of nontriangular structure nonlinear systems, which contain immeasurable states and unknown actuator faults (actuator loss-of-effectiveness and bias fault). It should be noted that if the existing approaches are employed for nontriangular structure nonlinear systems, the algebraic loop problem may occur. In this study, fuzzy logic systems are employed to approximate unknown nonlinear functions, and a fuzzy state observer is designed to estimate immeasurable states. Then, based on the property of performance function and simple barrier Lyapunov function design method, the prescribed output tracking error dynamic performance and the corresponding stability are guaranteed. By using the parameter estimation technique, a new fault compensation strategy is developed to relax the requirement that the efficiency indicator must be known. The stability of the closed-loop system is proved by using the Lyapunov function stability theory. Finally, a simulation example is given to validate the effectiveness of the proposed control strategy.

    更新日期:2018-08-02
  • Distributed Cooperative Learning Over Networks via Fuzzy Logic Systems: Performance Analysis and Comparison
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-10-16
    Pengfei Ren; Weisheng Chen; Hao Dai; Huaguang Zhang

    This paper studies a distributed machine learning problem by applying a distributed optimization algorithm over an undirected and connected communication network. Each node has its own fuzzy logic system (FLS) based machine whose weights are trained by the proposed FLS-based distributed cooperative learning (DCL) algorithm to reach the optimum of the global cost function. The training process utilizes the data that are distributed among different nodes and cannot be gathered at any node in the network. The main advantages of the FLS-based DCL algorithm are as follows: It has an exponential convergence; it requires a small amount of computation and communication at each iteration step; and the private and confidential information is protected without exchanging raw data between neighboring nodes. These advantages are verified by performing simulation experiments to compare the FLS-based DCL algorithm with the distributed average consensus based learning algorithm, the alternating direction method of multipliers based learning algorithm and the diffusion least-mean square algorithms.

    更新日期:2018-08-02
  • Event-Based Reliable Dissipative Filtering for T–S Fuzzy Systems With Asynchronous Constraints
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-10-12
    Yajuan Liu; Bao-Zhu Guo; Ju H. Park; Sangmoon Lee

    In this paper, event-triggered reliable dissipative filtering is investigated for a class of Takagi–Sugeno (T–S) fuzzy systems. First, a reliable event-triggered communication scheme is introduced to release sampled measurement outputs only if the variation of the sampled vector exceeds a prescribed threshold condition. Second, an asynchronous premise reconstruct method for T–S fuzzy systems is presented, which relaxes the assumption of the prior work that the premises of the plant and the filter are synchronous. Third, the resulting filtering error system is modeled under consideration of event-triggered communication, sensor failure, and asynchronous premise in a unified framework. By adopting the Lyapunov functional method and integral inequality approach, a delay-dependent criterion is developed to guarantee asymptotic stability for the filtering error systems and achieve strict$(Q, S,R)-\alpha$dissipativity. Consequently, suitable filters and the event parameters can be derived by solving a set of linear matrix inequalities. Finally, an example is given to show the effectiveness of the proposed method.

    更新日期:2018-08-02
  • Mm-OWA: A Generalization of OWA Operators
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-10-12
    Massimiliano Amarante

    We characterize those operators that satisfy the properties of monotonicity, permutation invariance, positive homogeneity, and translation invariance. As these operators do not necessarily satisfy comonotonic additivity, their class is larger than that of ordered weighted averaging (OWA) operators. We give a representation theorem for these operators, which shows, nonetheless, that this more general class can be constructed directly from that of OWA operators. In addition, we characterize the special classes consisting of operators that are either subadditive or superadditive. We suggest applications to the evaluation of complex systems.

    更新日期:2018-08-02
  • Granular Representation of Data: A Design of Families ofϵ-Information Granules
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-10-13
    Xiubin Zhu; Witold Pedrycz; Zhiwu Li

    Fuzzy clustering has emerged as one of the fundamental conceptual and algorithmic frameworks supporting the development of information granules. Generic fuzzy clustering such as fuzzy C-means (FCM) has been utilized in a broad range of applications. However, the constructs resulting from fuzzy clustering, namely a partition matrix and prototypes, are numeric and as such are not capable of fully capturing the essence of the overall data. In this study, we propose an alternative augmented way of building information granules by generating hypercube-like information granules. A collection of hypercubes is referred to as a family of ϵ-information granules. This family is constructed around numeric prototypes generated through a modified version of the FCM algorithm whose running time is linear with respect to the number of clusters. By admitting a certain level of information granularity$(\varepsilon)$, a collection of hypercubes is formed around the prototypes. The quality of information granules realized in this way is assessed by involving them in the granulation—degranulation process as well as determining a value of the coverage criterion. The level of information granularity and the number of the granular prototypes in the family of$\varepsilon $-information granules form an important design asset directly impacting the obtained coverage level of the data. The computational facet of the approach is stressed. It has been demonstrated that the granular enhancements of the description of data come with a very limited computing overhead. Experimental studies involve synthetic data as well as data coming from the UCI Machine Learning repository. The granular reconstruction capabilities delivered by the family of$\varepsilon $-information granules are discussed.

    更新日期:2018-08-02
  • A Mutual Information-Based Two-Phase Memetic Algorithm for Large-Scale Fuzzy Cognitive Map Learning
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-10-18
    Xumiao Zou; Jing Liu

    Various automatic learning algorithms have been proposed to learn fuzzy cognitive maps (FCMs), but most of them were only applied to learn small-scale FCMs and the learned maps obtained by such methods are usually much denser than the real maps. Learning FCMs requires the learning methods to not only determine the existence of links between concepts but also optimize the edge weights, which is the difficulty for FCM learning methods. Therefore, we propose a mutual information (MI)-based two-phase memetic algorithm (MA) for learning large-scale FCMs, termed as MIMA-FCM. In MIMA-FCM, the first phase is oriented to determine the existence of links between concepts by MI, which can reduce the search space significantly for MA, and then MA is used to optimize the edge weights according to the multiple observed response sequences in the second phase. Experiments on both synthetic and real-life data and the application for the gene regulatory network reconstruction problem demonstrate that the proposed method can not only find the plausible existence of links between concepts, but also optimize the edge weights rapidly. The comparison with existing algorithms shows that MIMA-FCM can learn large-scale FCMs with higher accuracy without expert knowledge.

    更新日期:2018-08-02
  • Adaptive Fuzzy Predictive Controller for a Class of Networked Nonlinear Systems With Time-Varying Delay
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-10-19
    Mohamed Hamdy; Sameh Abd-Elhaleem; M. A. Fkirin

    In this paper, an adaptive fuzzy predictive controller (AFPC) is designed and analyzed for a class of networked nonlinear systems with time-varying communication delay. The integration of the communication network in the control loops makes the nonlinear system has forward and feedback time-varying delays. The proposed controller compensates the network-induced delays in the forward and feedback channels. The structure of the AFPC consists of adaptive fuzzy logic control (AFLC) with state predictor located at the controlled plant and a remote adaptive smith predictive controller at the master node. The AFLC and the state predictor are utilized to identify the dynamic of the time-varying delay-free nonlinear plant in order to cancel the nonlinear term for the nonlinear control system in a canonical form. In the remote controller, adaptive smith predictor is employed to compensate the time-varying delay effect to achieve the desired tracking performance. Based on the Lyapunov theory, the stability of the closed-loop system is guaranteed in the presence of bounded external disturbance and time-varying delays. Simulated application of Van der Pol oscillator is provided to demonstrate the feasibility and effectiveness of the proposed scheme based on TrueTime toolbox.

    更新日期:2018-08-02
  • Adaptive Fuzzy Tracking Control Based Barrier Functions of Uncertain Nonlinear MIMO Systems With Full-State Constraints and Applications to Chemical Process
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-10-23
    Dong-Juan Li; Shu-Min Lu; Yan-Jun Liu; Da-Peng Li

    An adaptive control approach based on the fuzzy systems for a class of uncertain nonlinear multi-input multi-output (MIMO) systems is presented in this paper. This class of systems is in the nested multiple coupling structure and their states are constrained in the corresponding compact sets. The properties of the system structure are inevitable to bring about a complicated design and a difficult task. The fuzzy logic systems are employed to approximate the unknown functions of systems, and the decoupling backstepping way is proposed to design the stability controller and adaptation laws. Barrier Lyapunov functions (BLFs) are constructed in the backstepping design to guarantee that the constraint bounds are not violated. Based on Lyapunov analysis in barrier form, we can prove the stability of the closed-loop system. Two simulation examples are viewed to verify the feasibility of the approach.

    更新日期:2018-08-02
  • Data-Driven Elastic Fuzzy Logic System Modeling: Constructing a Concise System With Human-Like Inference Mechanism
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-10-26
    Jiangbin Zhang; Zhaohong Deng; Kup-Sze Choi; Shitong Wang

    The construction of fuzzy logic systems (FLSs) using data-driven techniques has become the most popular modeling approach. However, this approach still faces critical challenges, including the difficulty in obtaining concise models for high-dimensional data and generating accurate fuzzy rules to simulate human inference mechanism. To tackle these issues, a new FLS modeling framework called data-driven elastic FLS (DD-EFLS) is proposed in this paper. The DD-EFLS has two key characteristics. First, the fuzzy rules in the rule base can use different feature subspaces that are extracted from the original high-dimensional space to yield simple and accurate rules in feature spaces of lower dimensionality. Second, fuzzy inferences from various views are implemented by embedding different rules in the corresponding subspaces to imitate human inference mechanism. Based on the DD-EFLS framework, an elastic Takagi–Sugeno–Kang (TSK) FLS modeling method (ETSK-FLS) is proposed to train the elastic TSK FLS using the concise rules and a more human-like inference mechanism for modeling tasks based on high-dimensional datasets. The characteristics and advantages of the proposed framework and the ETSK-FLS method are validated experimentally using both synthetic and real-world datasets.

    更新日期:2018-08-02
  • Maximal-Discernibility-Pair-Based Approach to Attribute Reduction in Fuzzy Rough Sets
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-10-30
    Jianhua Dai; Hu Hu; Wei-Zhi Wu; Yuhua Qian; Debiao Huang

    Attribute reduction is one of the biggest challenges encountered in computational intelligence, data mining, pattern recognition, and machine learning. Effective in feature selection as the rough set theory is, it can only handle symbolic attributes. In order to overcome this drawback, the fuzzy rough set model is proposed, which is an extended model of rough sets and is able to deal with imprecision and uncertainty in both symbolic and numerical attributes. The existing attribute selection algorithms based on the fuzzy rough set model mainly take the angle of “attribute set,” which means they define the object function representing the predictive ability for an attribute subset with regard to the domain of discourse, rather than following the view of an “object pair.” Algorithms from the viewpoint of the object pair can ignore the object pairs that are already discerned by the selected attribute subsets and, thus, need only to deal with part of object pairs instead of the whole object pairs from the discourse, which makes such algorithms more efficient in attribute selection. In this paper, we propose the concept of reduced maximal discernibility pairs, which directly adopts the perspective of the object pair in the framework of the fuzzy rough set model. Then, we develop two attribute selection algorithms, named as reduced maximal discernibility pairs selection and weighted reduced maximal discernibility pair selection, based on the reduced maximal discernibility pairs. Experiment results show that the proposed algorithms are effective and efficient in attribute selection.

    更新日期:2018-08-02
  • Neighborhood Rough Filter and Intuitionistic Entropy in Unsupervised Tracking
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-11-02
    Debarati Bhunia Chakraborty; Sankar K. Pal

    This paper aims at developing a novel methodology for unsupervised video tracking by exploring the merits of neighborhood rough sets. A neighborhood rough filter is designed in this process for initial labeling of continuous moving object(s) even in the presence of several variations in different feature spaces. The locations and color models of the object(s) are estimated using their lower–upper approximations in spatio-color neighborhood granular space. Velocity neighborhood granules and acceleration neighborhood granules are then defined over this estimation to predict the object location in the next frame and to speed up the tracking process. A novel concept, namely, intuitionsistic entropy is introduced here, which consists of two new measures: neighborhood rough entropy and neighborhood probabilistic entropy to deal with the ambiguities that arise due to occurrence of overlapping/ occlusion in a video sequence. The unsupervised method of tracking is equally good even when compared with some of the state-of-the art partially supervised methods while showing superior performance during total occlusion.

    更新日期:2018-08-02
  • Adaptive Event-Triggered Fault Detection for Fuzzy Stochastic Systems With Missing Measurements
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-12-06
    Zhaoke Ning; Jinyong Yu; Yingnan Pan; Hongyi Li

    This paper discusses adaptive event-triggered fault detection filter design for fuzzy stochastic models with missing measurements. First, a novel event-triggered strategy is introduced, while an adaptive law is provided to adjust communication threshold dynamically. Compared with traditional event-triggered methods with fixed threshold, the proposed strategy is more effective on saving network communication resources. Second, a Bernoulli stochastic process is proposed to describe the measurement missing phenomenon, which always appears in real network environment. Then, an integrated fault detection model for fuzzy stochastic systems is constructed by taking network-induced delays, adaptive event-triggered strategy and missing measurements into account. A new method is provided to achieve mean-square asymptotical stability of residual model with one desired fault detection objective. Finally, simulation cases are introduced to verify the validity of the designed strategy.

    更新日期:2018-08-02
  • Autonomous Learning Multimodel Systems From Data Streams
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-11-01
    Plamen P. Angelov; Xiaowei Gu; José C. Príncipe

    In this paper, an approach to autonomous learning of a multimodel system from streaming data, named ALMMo, is proposed. The proposed approach is generic and can easily be applied also to probabilistic or other types of local models forming multimodel systems. It is fully data driven and its structure is decided by the nonparametric data clouds extracted from the empirically observed data without making any prior assumptions concerning data distribution and other data properties. All metaparameters of the proposed system are obtained directly from the data and can be updated recursively, which improves memory and calculation efficiencies of the proposed algorithm. The structural evolution mechanism and online data cloud quality monitoring mechanism of the ALMMo system largely enhance the ability of handling shifts and/or drifts in the streaming data pattern. Numerical examples of the use of ALMMo system for streaming data analytics, classification, and prediction are presented as a proof of the proposed concept.

    更新日期:2018-08-02
  • Weighting Models to Generate Weights and Capacities in Multicriteria Group Decision Making
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-11-01
    LeSheng Jin; Radko Mesiar; Gang Qian

    In multicriteria group decision-making problems, we need to determine the relative importances among criteria as well as among experts. When doing so, however, we often face the situations where consensuses within experts over different criteria need to be considered, and where uncertainties arise when experts do evaluations. Therefore, we need some special and reasonable methods to generate weights in such situations. In this study, three elaborately devised methods suggest ways to generate relative importance among experts, criteria, and the combination of them, respectively. The first one elicits the consensus extents within experts over different criteria, by which it can generate suitable weights among different criteria. The second one fully considers the uncertain nature when experts do evaluations, and proposes a fuzzy model which can generate weighting vector among experts according to the certainty degrees of valuations given by all the experts. In the last method, when relative importances among both experts and criteria are predetermined in the form of two capacities with dimensionnandm, respectively, we find an interesting mechanism to successfully melt them into onenm-dimensional capacity which is based on given cognitive strength and on the proposed concept of compromised active/passive consensus.

    更新日期:2018-08-02
  • Ordered Directionally Monotone Functions: Justification and Application
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-11-02
    Humberto Bustince; Edurne Barrenechea; Mikel Sesma-Sara; Julio Lafuente; Graçaliz Pereira Dimuro; Radko Mesiar; Anna Kolesárová

    In this paper, we introduce the notion of ordered directionally monotone function as a type of function which allows monotonicity along different directions in different points. In particular, these functions take into account the ordinal size of the coordinates of the inputs in order to fuse them. We show several examples of these functions and we study their properties. Finally, we present an illustrative example of an application which justifies the introduction and the study of the concept of ordered directional monotonicity.

    更新日期:2018-08-02
  • A Two-Phase Approach to Finding a Better Managerial Solution for Systems With Addition-Min Fuzzy Relational Inequalities
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-11-09
    Sy-Ming Guu; Jiajun Yu; Yan-Kuen Wu

    In the relevant literature, fuzzy relational inequalities with addition-min composition have been proposed to model the data transmission mechanism in a BitTorrent-like peer-to-peer file-sharing system. In this paper, we present a two-phase approach to find an optimal solution to data transmission that minimizes an associated function while its components are controlled to result in reduced network congestion. Numerical examples are given to illustrate the procedures of the two-phase approach.

    更新日期:2018-08-02
  • Robust Sliding Mode Control for T-S Fuzzy Systems via Quantized State Feedback
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-11-09
    Yanmei Xue; Bo-Chao Zheng; Xinghuo Yu

    This paper is concerned with the robust sliding mode control (SMC) problem for a class of T-S fuzzy systems subject to both matched and mismatched uncertainties. Different from the conventional T-S fuzzy SMC design approach, the quantized states rather than states themselves, are utilized for the control design. By the combination of the proposed zooming-out/zooming-in adjustment policy of the quantizer sensitivity, the quantized state feedback fuzzy sliding mode control scheme is developed to ensure the stabilization of the T-S fuzzy systems. Finally, some simulation results are presented to illustrate the effectiveness of the proposed approach.

    更新日期:2018-08-02
  • Fuzzy Fractional Quadratic Regulator Problem Under Granular Fuzzy Fractional Derivatives
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-12-15
    Marzieh Najariyan; Yi Zhao

    In this paper, a class of uncertain linear dynamical systems called fuzzy fractional linear dynamical systems is investigated. The aim is to find control inputs to keep the states of the fuzzy fractional dynamical systems near the zero in an optimal manner. The optimality criterion is in a form of a granular fuzzy integral whose integrand is a quadratic function of the state variables and control inputs. The fuzzy fractional dynamical system is described using fuzzy fractional differential equations (FFDEs). In order to achieve the aim, an effective approach for solving FFDEs should be at disposal. Due to some restrictions imposed by the previous approaches dealing with FFDEs, a new approach is proposed. The proposed approach is based on the granular derivative and the so-called relative-distance-measure fuzzy interval arithmetic. New definitions of fuzzy fractional derivatives and integral called left and right granular Riemann–Liouville fuzzy fractional derivatives, left and right granular Caputo fuzzy fractional derivatives, and the left and right granular fuzzy fractional integral are also presented. In addition, the concepts of granular fuzzy partial derivative and granular fuzzy chain rule are introduced. By the approximations of the granular fuzzy fractional integral and the granular Caputo fuzzy fractional derivative, the approximation solution to the FFDEs is obtained. Consequently, based on the new concepts and theorems, the solution to the fuzzy fractional quadratic regulator problem is given by a theorem. This paper closes with an example of regulating the motion of Boeing 747 in longitudinal direction with the presence of uncertainty in the initial conditions and the coefficients.

    更新日期:2018-08-02
  • Stability Analysis of Positive Polynomial Fuzzy-Model-Based Control Systems With Time Delay Under Imperfect Premise Matching
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-11-13
    Xiaomiao Li; Hak Keung Lam; Ge Song; Fucai Liu

    This paper deals with the stability and positivity analysis of polynomial-fuzzy-model-based (PFMB) control systems with time delay, which is formed by a polynomial fuzzy model and a polynomial fuzzy controller connected in a closed loop, under imperfect premise matching. To improve the design and realization flexibility, the polynomial fuzzy model and the polynomial fuzzy controller are allowed to have their own set of premise membership functions. A sum-of-squares-based stability analysis approach using the Lyapunov stability theory is employed to investigate the positivity and stability of the PFMB control systems and synthesize the polynomial fuzzy controller. In order to relax the stability results, we propose two methods: first, membership functions are considered as symbolic variables in the stability analysis; and second, the property of the membership functions and the boundary information of the membership functions are considered in the stability analysis. A simulation example is given to illustrate the effectiveness of the proposed approach.

    更新日期:2018-08-02
  • Observer-Based Fuzzy Adaptive Sensor Fault Compensation for Uncertain Nonlinear Strict-Feedback Systems
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-11-13
    Lili Zhang; Guang-Hong Yang

    This paper investigates the adaptive fuzzy tracking control problem for a class of uncertain nonlinear strict-feedback systems under sensor faults and unmeasured states. To address the challenges incurred by the partial loss of effectiveness of sensors, a novel observer-based adaptive fault-tolerant controller is designed. Different from the existing state observer design methods, the new state observer contains an adaptive fault compensation mechanism. By employing a cubic absolute-value Lyapunov function, it is proved that the proposed fault-tolerant controller guarantees that all the signals in the closed-loop system are bounded and the tracking performance of the system can be guaranteed even if the sensor fault occurs. Simulation results verify the effectiveness of the proposed method.

    更新日期:2018-08-02
  • User-Satisfaction-Aware Power Management in Mobile Devices Based on Perceptual Computing
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-11-13
    Pranab K. Muhuri; Prashant K. Gupta; Jerry M. Mendel

    Present day portable devices such as laptops, smartphones, etc., offer their users fastest processors, advanced operating systems, and numerous applications. However, a large section of the users are critical to the available battery capacity and its lifetime. This is because performance of the battery and its lifetime as perceived by the users are quite subjective in nature. It depends directly on user satisfactions, which are usually expressed in terms of words. So, in this paper, we propose a user-satisfaction-aware energy management approach, called “perceptual computer power management approach (Per-C PMA),” based on the technique of perceptual computing. At the heart of our technique is the perceptual computer that processes the linguistic input of the users to aid in the selection of a suitable processor frequency, which plays a significant role in the overall energy consumption of the systems. The Per-C PMA minimizes the energy consumption, while still keeping the user satisfied with the perceived system performance. The Per-C PMA achieves 1) reductions of 42.26% and 10.84% in power consumption, and 2) improvements in the overall satisfaction ratings of 16% and 10%, when compared to other existing power-saving schemes such as ON-DEMAND and human and application-driven frequency scaling for processor power efficiency, respectively. Per-C PMA is the first such application of Per-C on any hardware platform. It is implemented as Ubuntu scripts for end users and can be downloaded from:http://sau.ac.in/∼cilab/. We have also provided the MATLAB files so that interested researchers can use it in their research. For the ease of the users, the Ubuntu scripts and the MATLAB codes are given in the graphical user interface mode; a demo video on how to use the software is also provided on the webpage.

    更新日期:2018-08-02
  • Fault-Tolerant Aircraft Control Based on Self-Constructing Fuzzy Neural Networks and Multivariable SMC Under Actuator Faults
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-11-13
    Xiang Yu; Yu Fu; Peng Li; Youmin Zhang

    This paper presents a fault-tolerant aircraft control (FTAC) scheme against actuator faults. First, the upper bounds of the norms of the unknown functions are introduced, which contain actuator faults and model uncertainties. Subsequently, self-constructing fuzzy neural networks (SCFNNs) with adaptive laws are capable of obtaining the bounds. The bound estimation can reduce the computational burden with a lower amount of rules and weights, rather than the dynamic matrix approximation. Moreover, with the aid of SCFNNs, a multivariable sliding mode control (SMC) is developed to guarantee the finite-time stability of the handicapped aircraft. As compared to the existing intelligent FTAC techniques, the proposed method has twofold merits: fault accommodation can be promptly accomplished and decoupled difficulties can be overcome. Finally, simulation results from the nonlinear longitudinal Boeing 747 aircraft model illustrate the capability of the presented FTAC scheme.

    更新日期:2018-08-02
  • Observer-Based Composite Adaptive Fuzzy Control for Nonstrict-Feedback Systems With Actuator Failures
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-11-16
    Lijie Wang; Michael V. Basin; Hongyi Li; Renquan Lu

    This paper studies the observer-based adaptive fuzzy tracking control problem for a general class of multi-input-single-output nonstrict-feedback systems subject to unmeasured states and actuator failures. For actuator failures, both cases of lock-in-place and loss of effectiveness are synchronously considered. To handle the unknown nonlinear functions, fuzzy logic systems are employed. By constructing a fuzzy observer and a serial-parallel estimation model, the unmeasured states are estimated and the accuracy of approximating the unknown functions is improved. Moreover, taking into account the prediction error between the fuzzy observer and the serial-parallel estimation model, a novel composite fuzzy output-feedback control scheme is developed. Unlike some existing control schemes for systems with actuator failures, the developed control scheme allows one to avoid the problem of “explosion of complexity” and improve the approximation performance. It is proved that all signals in the system are bounded and the tracking error converges to a small neighborhood of the origin by choosing appropriate parameters. Finally, the effectiveness of the proposed method is confirmed via simulation examples with actuator failures.

    更新日期:2018-08-02
  • Interpretability Constraints for Fuzzy Modeling Implemented by Constrained Particle Swarm Optimization
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2017-11-16
    George E. Tsekouras; John Tsimikas; Christos Kalloniatis; Stefanos Gritzalis

    In this paper certain interpretability criteria are taken into account in order to extract a set of linear inequality constraints for enhancing the fuzzy model interpretability. Among others, the criteria of model distinguishability, completeness, compactness, and fuzzy set sharing between rules are considered. To support distinguishability, the distances between fuzzy set centers are lower bounded and the widths are manipulated as to control the overlap between fuzzy sets. Sufficient conditions are given to satisfy the completeness criterion, whereas the compactness requirement is addressed by comparing models with different number of rules. Finally, fuzzy set sharing between rules is achieved through a model optimization procedure that involves fuzzy set merging. It turns out that the feasible region is a compact and convex set. The tradeoff between interpretability and accuracy is established by minimizing the model's square error over the feasible region through constrained particle swarm optimization. The method is tested using a number of high-dimensional datasets and conducting two kinds of experiments. The first focuses on interpretability. The second studies the accuracy by comparing the method to other algorithms that perform unconstrained optimization, using nonparametric statistics.

    更新日期:2018-08-02
  • Intuitionistic Fuzzy Rough Set-based Granular Structures and Attribute Subset Selection
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-08-02
    Anhui Tan; Wei-Zhi Wu; Yuhua Qian; Jiye Liang; Jinkun Chen; Jinjin Li

    Attribute subset selection is an important issue in data mining and information processing. However, most auto- matic methodologies consider only the relevance factor between samples while ignoring the diversity factor. This may not allow the utilization value of hidden knowledge to be exploited. For this reason, in this study, we employ a hybrid model named intuitionistic fuzzy (IF) rough set to overcome this limitation. The model combines the technical advantages of rough set and IF set and can effectively consider the above-mentioned statistical factors. First, fuzzy information granules based on IF relations are defined, which are used to characterize the hierarchical structures of the lower and upper approximations of IF rough sets within the framework of granular computing. Then, the computation of IF rough approximations and knowledge reduc- tion in IF information systems are investigated. Third, based on the approximations of IF rough sets, significance measures are developed to evaluate the approximation quality and classification ability of IF relations. Furthermore, a forward heuristic algorith- m for finding one optimal reduct of IF information systems is developed using these measures. Finally, numerical experiments are conducted on public data sets to examine the efficiency of the proposed algorithm in terms of the number of selected attributes, computational time, and classification accuracy.

    更新日期:2018-08-02
  • iPatch: A Many-Objective Type-2 Fuzzy Logic System for Field Workforce Optimisation
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-08-02
    Andrew J. Starkey; Hani Hagras; Sid Shakya; Gilbert Owusu

    Employing effective optimisation strategies in organisations with large workforces can have a clear impact on costs, revenues, and customer satisfaction. This is particularly true for organisations that employ large field workforces, such as utility companies. Ensuring each member of the workforce is fully utilised is a challenging problem as there are many factors that can impact the organisation's overall performance. We have developed a system that optimises to make sure we have the right engineers, in the right place, at the right time, with the right skills. This system is currently deployed to help solve real-world optimisation problems, which means there are many objectives to consider when optimising, and there is much uncertainty in the environment. The latest version of the system uses a multi-objective genetic algorithm as its core optimisation logic, with modifications such as Fuzzy Dominance Rules (FDRs), to help overcome the issues associated with many-objective optimisation. The system also utilises genetically optimised type-2 fuzzy logic systems to better handle the uncertainty in the data and modelling. This paper shows the genetically optimised type-2 fuzzy logic systems producing better results than the crisp value implementations in our application. We also show that we can help address the weaknesses in the standard NSGA-II dominance calculations by using FDRs. The impact of this work can be measured in a number of ways; productivity benefit of £1million a year, the reduction of over 2,500 metric tonnes of CO2 and a possible prevention of over 100 serious injuries and fatalities on the UK's roads.

    更新日期:2018-08-02
  • Similarity Measures for Closed General Type-2 Fuzzy Sets: Overview, Comparisons, and a Geometric Approach
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-08-02
    Dongrui Wu; Jerry M. Mendel

    The similarity between two fuzzy sets (FSs) is a very important concept in fuzzy logic. As the research interest on general type-2 (GT2) FSs has increased recently, many similarity measures for them have also been proposed. This paper gives a comprehensive overview of existing similarity measures for GT2 FSs, points out their limitations, and, using an intuitive geometric explanation, proposes a Jaccard similarity measure for GT2 FSs, which is an extension of the popular Jaccard similarity measure for type-1 and interval type-2 FSs. The fundamental difference between the proposed Jaccard similarity measure for GT2 FSs and all existing similarity measures is that the Jaccard similarity measure considers the overall geometries of two GT2 FSs, and does not depend on a specific representation of the GT2 FSs, whereas all existing similarity measures for GT2 FSs depend either on the vertical slice representation or the$\alpha$-plane representation. We show that the Jaccard similarity measure for GT2 FSs satisfies four properties of a similarity measure, and demonstrate its reasonableness using two examples.

    更新日期:2018-08-02
Some contents have been Reproduced with permission of the American Chemical Society.
Some contents have been Reproduced by permission of The Royal Society of Chemistry.
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