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  • Towards a Fuzzy Logic System Based on General Forms of Interval Type-2 Fuzzy Sets
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-12
    Gonzalo Ruiz-Garcia; Hani Hagras; Hector Pomares; Ignacio Rojas

    Recent years have witnessed a widespread in the use of interval type-2 fuzzy logic systems (IT2 FLSs) in real world applications. It has been shown recently that interval type-2 fuzzy sets (IT2 FSs) are more general than intervalvalued fuzzy sets (IV FSs) [1]. Hence, there is a need to explore the capabilities of the more general forms of IT2 FSs (beyond IV FSs) and the applications areas they will be more suitable for. In addition, there is a need to develop the theory of the general forms of IT2 FLSs (gfIT2 FLSs), which employ IT2 FSs which are not equivalent to IV FSs and can have nonconvex secondary membership functions. Although these systems could be considered within the scope of General Type-2 Fuzzy Logic Systems (GT2 FLSs), the practical implementation of GT2 FLSs has traditionally required the secondary membership functions to be convex and normal type-1 fuzzy sets (T1 FSs). In addition, the type-reduction operation still presents a challenge for GT2 FLSs because of its computational complexity. In this paper, we present a complete framework for a type-2 FLS that uses the most recent perception of IT2 FSs (the so called general forms of interval type-2 fuzzy sets, gfIT2 FSs), whose secondary grades can be non-convex T1 FSs. This framework includes new equations for the meet and join operations of gfIT2 FSs, as well as a new type reduction procedure for the type-2 FLS involving gfIT2 FSs. In addition, we present the type-2 FLS operation for singleton and non-singleton fuzzification. We will introduce the various operations employed within a gfIT2 FLSs, from fuzzification (including singleton and nonsingleton) to inference, type-reduction and defuzzification. We will also present two examples in which these gfIT2 FSs arise naturally when modelling sonar sensors input noise and the antecedents/consequents from a survey including different users.

    更新日期:2019-02-13
  • Reliability Modelling for Repairable Systems with Stochastic Lifetimes and Uncertain Repair Times
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 
    Liu Ying; Qu Zhigang; Li Xiaozhong; An Yang; Yin Wuliang

    In actual industrial systems, there are often situations where the fault data is sufficient while the repair data is insufficient. It is not appropriate to treat the system as a stochastic system or an uncertain system in this case. So in this paper, the lifetimes of components are assumed to be random variables and the repair times of components are assumed to be uncertain variables. The entire system can be regarded as an uncertain random repairable system accordingly. Based on chance theory, some common reliability indexes of repairable system, such as availability, steady state failure frequency, mean up time, mean down time, are redefined and some properties are also presented. Then the reliability mathematical models of repairable series systems, repairable parallel systems, repairable series-parallel systems and repairable parallel-series systems are established, respectively. Furthermore, the formulas of reliability indexes of each repairable system are arrived. Finally, some numerical examples are presented to illustrate the calculation of the reliability indexes.

    更新日期:2019-02-11
  • Pinning Event-Triggered Sampling Control for Synchronization of T-S Fuzzy Complex Networks With Partial and Discrete-Time Couplings
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-08
    Ruimei Zhang; Deqiang Zeng; Ju H. Park; Yajuan Liu; Shouming Zhang

    This paper focuses on the synchronization problem of T-S fuzzy complex networks with partial and discrete-time couplings (PDTCs) via event-triggered sampling control (ETSC). Different from traditional control methods, a more general and practical event-triggered communication scheme with nonuniform sampling is newly designed for T-S fuzzy complex networks. Then, a Lyapunov-Krasovskii functional (LKF) with a novel input-delay-product-type (IDPT) term is presented. The IDPT term can fully capture the information of the nonlinear functions and the actual sampling pattern. Based on the new IDPT LKF, less conservative synchronization criteria are derived. Meanwhile, by solving a set of linear matrix inequalities (LMIs), the desired pinning control gains are precisely obtained. Simulation examples are provided to illustrate the effectiveness and superiorities of the proposed results.

    更新日期:2019-02-11
  • Type-2 fuzzy envelope of hesitant fuzzy linguistic term set: a new representation model of comparative linguistic expression
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-08
    Yaya Liu; Rosa M. Rodriguez; Hani Hagras; Hongbin Liu; Keyun Qin; Luis Martinez

    The use of hesitant fuzzy linguistic term sets contributes to the elicitation of comparative linguistic expressions in decision contexts when experts hesitate among different linguistic terms to provide their assessments. Since the existing representation models for linguistic expressions based on hesitant fuzzy linguistic term sets do not consider properly the uncertainty caused by the inherent vagueness of such linguistic expressions, it is necessary to improve their modeling to cope with such vagueness. In this paper, we propose a new fuzzy envelope for the hesitant fuzzy linguistic term sets in form of type-2 fuzzy sets for representing comparative linguistic expressions. Such an envelope overcomes the limitation of existing representations in coping with inherent uncertainties and facilitates the processes of computing with words for linguistic decision making problems dealing with comparative linguistic expressions.

    更新日期:2019-02-11
  • Fuzzy Support Vector Machine with Relative Density Information for Classifying Imbalanced Data
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-08
    Hualong Yu; Sun Changyin; Xibei Yang; Shang Zheng; Haitao Zou

    Fuzzy support vector machine (FSVM) has been combined with class imbalance learning (CIL) strategies to deal with the problem of classifying skewed data. However, the existing approaches hold several inherent drawbacks, causing the inaccurate prior data distribution estimation, further decreasing the quality of the classification model. To solve this problem, we present a more robust prior data distribution information extraction method named relative density, and two novel FSVM-CIL algorithms based on the relative density information in this paper. In our proposed algorithms, a K-nearest neighbors-based probability density estimation (KNN-PDE) alike strategy is utilized to calculate the relative density of each training instance. In particular, the relative density is irrelevant with the dimensionality of data distribution in feature space, but only reflects the significance of each instance within its class, hence it is more robust than the absolute distance information. In addition, it can better seize the prior data distribution information, no matter the data distribution is easy or complex. Even for the data with small injunctions or a large class overlap, the relative density information can reflect its details well. We evaluated the proposed algorithms on an amount of synthetic and real-world imbalanced data sets. The results show that our proposed algorithms obviously outperform to some previous work, especially on those data sets with sophisticated distributions.

    更新日期:2019-02-11
  • Interval Observer Design for Discrete-Time Uncertain Takagi-Sugeno Fuzzy Systems
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-07
    Jitao Li; Zhenhua Wang; Yi Shen; Yan Wang

    This paper proposes a novel interval observer design method for discrete-time Takagi-Sugeno fuzzy systems with parametric uncertainty, disturbances and measurement noise. We present a new structure of interval observer with more design parameters, which can be used to broaden the application scope of interval observer design. For improving the accuracy of interval estimation, an $L_{\infty}$ norm-based approach is used in the design of interval observer to attenuate the effect of the unknown disturbances, noise and parametric uncertainty. Furthermore, the design conditions are formulated into a set of linear matrix inequalities, which can be efficiently solved. Numerical simulations are given to illustrate the effectiveness of the proposed method.

    更新日期:2019-02-08
  • Event-triggered Adaptive Fuzzy Control for Uncertain Strict-feedback Nonlinear Systems with Guaranteed Transient Performance
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-07
    Xiaohang Su; Zhi Liu; Guanyu Lai; Yun Zhang; C. L. Philip Chen

    In this paper, we shall address the problem of guaranteeing transient performance in adaptive tracking control of uncertain strict-feedback nonlinear systems within the framework of event-triggered control. Note that most of the existing literature about event-triggered control focus on solving exactly known systems or completely parametric systems, but still no result available in handling more general systems with unparametrizable uncertainties. Such formidable issue is successfully resolved in this paper by using the fuzzy logic systems to approximate real-time value of the unknown functions, and the square of the norm of fuzzy weight vector is applied to the backstepping recursive design. Furthermore, by constructing a class of new Lyapunov candidates, the chattering phenomenon caused by sign function can be avoided, while the transient performance in terms of the tracking error can also be achieved to be an explicit function with the user-defined parameters. It is further proved that our proposed scheme ensures the global boundedness of all the closed-loop signals. Finally, the simulation results verify the effectiveness of the established theoretic.

    更新日期:2019-02-08
  • Uncertainty measurement for a fuzzy relation information system
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-07
    Zhaowen Li; Pengfei Zhang; Xun Ge; Ningxin Xie; Gangqiang Zhang; Chingfeng Wen

    A fuzzy relation information system may be viewed as an information system with fuzzy relations. Uncertainty measurement is a critical evaluating tool. This paper investigates uncertainty measurement for a fuzzy relation information system. The concept of information structures in a fuzzy relation information system is first described by using set vectors. Then, dependence between information structures in a fuzzy relation information system is given. Next, the axiom definition of granularity measurement of uncertainty for fuzzy relation information systems is proposed by means of its information structures, and based on this axiom definition, information granulation and rough entropy in a fuzzy relation information system are proposed. Moreover, information entropy, information amount, joint entropy and condition entropy in a fuzzy relation information system are also considered. Finally, to show the feasibility of the proposed measures for uncertainty of a fuzzy relation information system, effectiveness analysis is conducted from the angle of statistics. These results will be helpful for understanding the essence of uncertainty in a fuzzy relation information system.

    更新日期:2019-02-08
  • Linguistic interval-valued Atanassov intuitionistic fuzzy sets and their applications to group decision-making problems
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-06
    Harish Garg; Kamal Kumar

    In this paper, we proposed the concept of a linguistic interval-valued Atanassov intuitionistic fuzzy set (LIVAIFS), whose membership and non-membership degree is represented by the interval-valued linguistic terms, for better dealing with the imprecise and uncertain information during the decision-making process. In it, firstly some operational laws, score and accuracy functions of LIVAIFS are defined with a brief study of related properties. Then, based on these operational laws, several aggregating operators are proposed to aggregate the linguistic interval-valued Atanassov intuitionistic fuzzy (LIVAIF) information. Some properties and inequalities are established to show the effectiveness and validity of proposed operators. Furthermore, a group decision-making approach, based on proposed operators, has been presented to solve the multi-attribute group decision-making problems under LIVAIF environment. Finally, an illustrative example has been presented to demonstrate the approach.

    更新日期:2019-02-07
  • Online Performance-Based Adaptive Fuzzy Dynamic Surface Control for Nonlinear Uncertain Systems Under Input Saturation
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-07-13
    Peng Wang; Xiaobing Zhang; Jihong Zhu

    As an extension of the conventional prescribed tracking performance constraint problem, this paper proposes a novel online tracking performance constrained control combined with the dynamic surface control (DSC) technique for a class of strict-feedback nonlinear systems with unmeasurable states and input saturation. Using fuzzy logic systems to approximate the unknown nonlinear functions, a high-gain fuzzy observer is designed for state estimation. In the development of DSC control, a serial-parallel estimation model is introduced to analyze the effect of input saturation, and a compensation term is added. To deal with the singularity problem of the prescribed performance constrained method, a novel online performance function is investigated to obtain a satisfactory tracking performance. Under the proposed control scheme, the stability and boundedness of all closed-loop signals are confirmed via Lyapunov synthesis. Finally, numerical simulation results illustrate the effectiveness of the proposed scheme.

    更新日期:2019-02-06
  • A Consensus Model for Large-Scale Linguistic Group Decision Making With a Feedback Recommendation Based on Clustered Personalized Individual Semantics and Opposing Consensus Groups
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-07-19
    Cong-Cong Li; Yucheng Dong; Francisco Herrera

    In linguistic large-scale group decision making (LSGDM), it is often necessary to achieve a consensus. Particularly, when computing with words and linguistic decision, we must keep in mind that words mean different things to different people. Therefore, to represent the specific semantics of each individual, we need to consider the personalized individual semantics (PIS) model in linguistic LSGDM. In this paper, we propose a consensus model based on PIS for LSGDM. Specifically, a PIS process to obtain the individual semantics of linguistic terms with linguistic preference relations is introduced. A consensus process based on PIS, including the consensus measure and feedback recommendation phases, is proposed to improve the willingness of decision makers who follow the suggestions to revise their preferences in order to achieve a consensus in linguistic LSGDM problems. The consensus measure defines two opposing consensus groups with respective acceptable and unacceptable consensus. In the feedback recommendation phase, a PIS-based clustering method to get decision makers with similar individual semantics is proposed. Recommendation rules design a feedback for decision makers with unacceptable consensus, finding suitable moderators from the decision makers with acceptable consensus based on cluster proximity.

    更新日期:2019-02-06
  • A Goal-Programming-Based Heuristic Approach to Deriving Fuzzy Weights in Analytic Form from Triangular Fuzzy Preference Relations
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-07-02
    Zhou-Jing Wang

    Triangular fuzzy preference relations (TFPRs) have long been viewed as an effective framework to express subjective preferences with ambiguity in fuzzy multicriteria decision-making systems. This paper focuses on solving two important and challenging issues: First, how to judge the quality of nondiagonal preferences in a TFPR; and second, how to find an analytic solution of optimal fuzzy weights for consistent TFPRs and an analytic solution of heuristic fuzzy weights for inconsistent TFPRs. This paper analyzes two existing normalization models of triangular fuzzy weights and illustrates their deficiency. Notions of normalized triangular fuzzy multiplicative weights (TFMWs) and basic TFMWs are then introduced and used to characterize a group of triangular fuzzy weight vectors with equivalency. This paper proposes transformation models among triangular fuzzy weights, normalized TFMWs and basic TFMWs, and employs basic TFMWs to define consistent TFPRs. Some important properties are presented for the fuzziness of a consistent TFPR and its corresponding basic TFMWs. These properties are subsequently used to develop a two-step approach consisting of three-goal programming (GP) models to find basic TFMWs of consistent TFPRs. By using the Lagrangian multiplier method, this paper finds analytic solutions of the three GP models and obtains optimized basic TFMWs denoted by three computation formulas for consistent TFPRs. By changing the constraints of the three GP models and using heuristics, three concise and visualized formulas are devised, respectively, to obtain the lower and upper support bounds and the modal values of heuristic-based TFMWs for any TFPR. A numerical example comprising of a consistent TFPR and two inconsistent TFPRs is supplied and a comparative study is conducted to show that the two challenging issues are reasonably solved, and a hierarchical fuzzy multicriteria decision-making example is offered to illustrate the applicability of the proposed fuzzy priority model.

    更新日期:2019-02-06
  • Finite-Time Stabilization for Discontinuous Interconnected Delayed Systems via Interval Type-2 T–S Fuzzy Model Approach
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-07-13
    Nannan Rong; Zhanshan Wang; Huaguang Zhang

    This paper investigates the finite-time stabilization for a class of interconnected systems with nonlinear discontinuous interconnections in which the time-varying delay are considered. By utilizing the universal approximation ability of the fuzzy model, a unified interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy-model-based interconnected delayed system is provided. Then, in order to solve the existence of solution for the concerned system with discontinuous right-hand side, the Filippov solutions are defined based on differential inclusion theory and set-valued analysis. Furthermore, by the IT2 T–S fuzzy model approach, a delayed state feedback controller equipped with discontinuous term and time-varying delays term is proposed. According to the classical finite time stability theory and generalized Lyapunov approach, finite-time stabilization for the discontinuous interconnected delayed system is achieved, and the estimate of settling time is given. Moreover, when the detailed information of time-varying delays is unknown, the finite-time stabilization is also realized via another improved controller, which only depends upon the upper bound of time-varying delays. Finally, the proposed methodologies are illustrated by a numerical example.

    更新日期:2019-02-06
  • Efficient Robust Fuzzy Model Predictive Control of Discrete Nonlinear Time-Delay Systems via Razumikhin Approach
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-07-02
    Long Teng; Youyi Wang; Wenjian Cai; Hua Li

    In this paper, two efficient robust fuzzy model predictive control algorithms are investigated for discrete nonlinear systems with multiple time delays and bounded disturbances. The famous Takagi–Sugeno (T–S) fuzzy systems are utilized to represent nonlinear systems. Instead of the Lyapunov–Krasovskii functional, the Lyapunov–Razumikhin function is adopted to deal with time delays because it involves invariant sets in the original state space of the system. A sequence of explicit control laws corresponding to a sequence of constraint sets are computed offline so that the online computational burden associated with the classical model predictive control algorithms is significantly reduced. In particular, the set invariance theory behind the Razumikhin approach, which is more complicated than the one for nondelayed systems, is directly observed. Additionally, it is proved that all (delayed) states can enter the terminal set in finite time. Moreover, robust positive invariance and input-to-state stability for time-delay systems concerning disturbances are realized. Additionally, an online optimization algorithm is also provided based on the offline computed ellipsoidal sets. Therefore, the conservatism induced by the Razumikhin approach is relaxed, while the computational cost is not significantly increased.

    更新日期:2019-02-06
  • Omitting Types Theorem for Fuzzy Logics
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-07-13
    Petr Cintula; Denisa Diaconescu

    In this paper, we generalize the omitting types theorem, an important result of classical model theory, for a wide class of fuzzy logics, containing the prominent logics of left-continuous t-norms and uninorms.

    更新日期:2019-02-06
  • A Novel Fuzzy Observer-Based Steering Control Approach for Path Tracking in Autonomous Vehicles
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-07-13
    Changzhu Zhang; Jinfei Hu; Jianbin Qiu; Weilin Yang; Hong Sun; Qijun Chen

    In this paper, the problem of steering control is investigated for vehicle path tracking in the presence of parametric uncertainties and nonlinearities. In practice, the vehicle mass varies due to the number of passengers or amount of payload, while the vehicle velocity also changes during normal cruising, which significantly influences vehicle dynamics. Moreover, the vehicle dynamics are strongly nonlinear caused by the tire/road forces under different road surface conditions. With fuzzy modeling method, the original nonlinear path tracking system with parameter variations is first formulated as a T–S fuzzy model with additive norm-bounded uncertainties, and then an approach to the fuzzy observer-based output feedback steering control for vehicle dynamics is proposed under a fuzzy Lyapunov function framework. By employing matrix inequality convexifying techniques, a sufficient condition is developed in the form of linear matrix inequalities such that the closed-loop path tracking error system is asymptotically stable with a guaranteed $\mathcal {H}_{\infty }$ level. Finally, the effectiveness of the proposed fuzzy observer-based output feedback controller is demonstrated in Carsim/Matlab joint simulation environment, via which the advantage of a T–S fuzzy observer-based output controller over the closed-loop driver model embedded in Carsim is also shown with parametric uncertainties and nonlinearities.

    更新日期:2019-02-06
  • Fuzzy Transfer Learning Using an Infinite Gaussian Mixture Model and Active Learning
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-07-19
    Hua Zuo; Jie Lu; Guangquan Zhang; Feng Liu

    Transfer learning is gaining considerable attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a related domain. Fuzzy transfer learning, which is based on fuzzy system (especially fuzzy rule-based models), has been developed because of its capability to deal with the uncertainty in transfer learning. However, two issues with fuzzy transfer learning have not yet been resolved: choosing an appropriate source domain and efficiently selecting labeled data for the target domain. This paper proposes an innovative method based on fuzzy rules that combines an infinite Gaussian mixture model (IGMM) with active learning to enhance the performance and generalizability of the constructed model. An IGMM is used to identify the data structures in the source and target domains providing a promising solution to the domain selection dilemma. Further, we exploit the interactive query strategy in active learning to correct imbalances in the knowledge to improve the generalizability of fuzzy learning models. Through experiments on synthetic datasets, we demonstrate the rationality of employing an IGMM and the effectiveness of applying an active learning technique. Additional experiments on real-world datasets further support the capabilities of the proposed method in practical situations.

    更新日期:2019-02-06
  • Design of Hidden-Property-Based Variable Universe Fuzzy Control for Movement Disorders and Its Efficient Reconfigurable Implementation
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-07-13
    Shuangming Yang; Bin Deng; Jiang Wang; Chen Liu; Huiyan Li; Qianjin Lin; Chris Fietkiewicz; Kenneth A. Loparo

    One of the challenging problems in real-time control of movement disorders is the effective handling of time-variant brain activities that involve stochastic functional networks with nonlinear dynamics. For such challenges in neuromodulation tasks, fuzzy logic control (FLC) has shown significant potential. The objective of this paper is to present a FLC-based strategy to treat pathological symptoms of movement-disorder with higher performance. The strategy is two-fold: first, develop a design methodology for the FLC system that can robustly control pathological conditions and significantly improve control performance; and second, develop a hardware-efficient implementation for real-time neuromodulation applications. To enhance control performance, a hidden variable in the neural network that can be estimated using an unscented Kalman filter is identified as a feedback variable. In comparison with state-of-the-art schemes, the proposed design can adaptively optimize the control signals without requiring particular information of the controlled plant, thus avoiding repeated determinations of controller parameters. A field-programmable gate array is used for the reconfigurable realization of the entire control strategy based on a modification of the original neural network. The presented design, with enhanced control performance and higher hardware efficiency, has significant potential for clinical treatment of movement disorders and offers a new perspective on applications in the fields of neural control engineering and brain–machine interfaces.

    更新日期:2019-02-06
  • Consensus Building With Individual Consistency Control in Group Decision Making
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-07-13
    Cong-Cong Li; Rosa M. Rodríguez; Luis Martínez; Yucheng Dong; Francisco Herrera

    The individual consistency and the consensus degree are two basic measures to conduct group decision making with reciprocal preference relations. The existing frameworks to manage individual consistency and consensus degree have been investigated intensively and follow a common resolution scheme composed by the two phases: the consistency improving process, and the consensus reaching process. But in these frameworks, the individual consistency will often be destroyed in the consensus reaching process, leading to repeat the consistency improving process, which is time consuming. In order to avoid repeating the consistency improving process, a consensus reaching process with individual consistency control is proposed in this paper. This novel consensus approach is based on the design of an optimization-based consensus rule, which can be used to determine the adjustment range of each preference value guaranteeing the individual consistency across the process. Finally, theoretical and numerical analysis are both used to justify the validity of our proposal.

    更新日期:2019-02-06
  • General Type-2 Radial Basis Function Neural Network: A Data-Driven Fuzzy Model
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-07-23
    Adrian Rubio-Solis; Patricia Melin; Uriel Martinez-Hernandez; George Panoutsos

    This paper proposes a new General Type-2 Radial Basis Function Neural Network (GT2-RBFNN) that is functionally equivalent to a GT2 Fuzzy Logic System (FLS) of either Takagi-Sugeno-Kang (TSK) or Mamdani type. The neural structure of the GT2-RBFNN is based on the α-planes representation, in which the antecedent and consequent part of each fuzzy rule uses GT2 Fuzzy Sets (FSs). To reduce the iterative nature of the Karnik-Mendel algorithm, the Enhaned-Karnik-Mendel (EKM) type-reduction and three popular direct-defuzzification methods, namely the 1) Nie-Tan approach (NT), the 2) Wu-Mendel uncertain bounds method (WU) and the 3) Biglarbegian-Melek-Mendel algorithm (BMM) are used. Hence, this paper provides four different architectures of the GT2-RBFNN and their parametric optimisation. Such optimisation is a two-stage methodology that first implements an Iterative Information Granulation (IIG) approach to estimate the antecedent parameters of each fuzzy rule. Secondly, each consequent part and the fuzzy rule base of the GT2-RBFNN is optimised using an Adaptive Gradient Descent method (AGD) respectively. A number of popular benchmark data sets, the identification of a nonlinear system and the prediction of chaotic time series are considered. The reported comparative analysis of experimental results is used to evaluate the performance of the suggested GT2 RBFNN with respect to other popular methodologies.

    更新日期:2019-02-06
  • Fuzzy Rule-Based Domain Adaptation in Homogeneous and Heterogeneous Spaces
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-07-09
    Hua Zuo; Jie Lu; Guangquan Zhang; Witold Pedrycz

    Domain adaptation aims to leverage knowledge acquired from a related domain (called a source domain) to improve the efficiency of completing a prediction task (classification or regression) in the current domain (called the target domain), which has a different probability distribution from the source domain. Although domain adaptation has been widely studied, most existing research has focused on homogeneous domain adaptation, where both domains have identical feature spaces. Recently, a new challenge proposed in this area is heterogeneous domain adaptation where both the probability distributions and the feature spaces are different. Moreover, in both homogeneous and heterogeneous domain adaptation, the greatest efforts and major achievements have been made with classification tasks, while successful solutions for tackling regression problems are limited. This paper proposes two innovative fuzzy rule-based methods to deal with regression problems. The first method, called fuzzy homogeneous domain adaptation, handles homogeneous spaces while the second method, called fuzzy heterogeneous domain adaptation, handles heterogeneous spaces. Fuzzy rules are first generated from the source domain through a learning process; these rules, also known as knowledge, are then transferred to the target domain by establishing a latent feature space to minimize the gap between the feature spaces of the two domains. Through experiments on synthetic datasets, we demonstrate the effectiveness of both methods and discuss the impact of some of the significant parameters that affect performance. Experiments on real-world datasets also show that the proposed methods improve the performance of the target model over an existing source model or a model built using a small amount of target data.

    更新日期:2019-02-06
  • From Fuzzy Sets to Interval-Valued and Atanassov Intuitionistic Fuzzy Sets: A Unified View of Different Axiomatic Measures
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-07-12
    Inés Couso; Humberto Bustince

    This paper examines a broad collection of axiomatic definitions from various and diverse contexts within the domain of fuzzy sets (FS) to evaluate their respective extensions to the case of interval-valued fuzzy (IVF) sets and intuitionistic fuzzy (IF) sets from a purely formal point of view. We conclude that a large number of such extensions follow similar formal procedures. This fact allows us to formulate a general procedure that encompasses all the reviewed extensions as particular cases of it. The new general formulation allows us to identify three different procedures to derive the corresponding extension to the field of IVF or IF sets from a specific real-valued measure in the context of FSs. These three processes agglutinate a multitude of particular constructions found in the literature.

    更新日期:2019-02-06
  • Consistency Measures of Linguistic Preference Relations With Hedges
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-07-13
    Hai Wang; Zeshui Xu; Xiao-Jun Zeng; Huchang Liao

    Modeling linguistic information is vital for qualitative decision making (QDM). Compared with single linguistic terms, the complex linguistic expressions (CLEs) are more powerful and flexible to express linguistic opinions under uncertainties. Among the existing types of CLEs, the linguistic terms with weakened hedges (LTWHs), which focus on the uncertainty of using single terms, can be used to model the linguistic expressions in natural languages. This paper concentrates on the application of LTWHs in the framework of QDM with preference relations. The concept of linguistic preference relations with hedges is presented after a new computational model of LTWHs is formed. Some consistency concepts, such as weak consistency and additive consistency, are then defined and their properties are studied. Theories and algorithms for consistency checking and improving are proposed. Finally, the availability of the proposed technique is demonstrated by a real application. Different from many studies related to consistency measures, we make use of fuzzy weighted digraphs to develop the theories and algorithms in a visible manner. Moreover, for consistency improving, the degree of consistency is measured by linguistic terms rather than numerical values so that the threshold of satisfactory consistency is interpretable.

    更新日期:2019-02-06
  • Noise Robust Multiobjective Evolutionary Clustering Image Segmentation Motivated by the Intuitionistic Fuzzy Information
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-07-02
    Feng Zhao; Jiulun Fan; Hanqiang Liu; Rong Lan; Chang Wen Chen

    Images are always contaminated by noise, increasing uncertainty. Fuzzy set (FS) theory is a useful tool for dealing with uncertainty in images. When comparing with the FS, an intuitionistic fuzzy set (IFS) can better describe the blurred characteristic in images due to the membership, nonmembership, and hesitation degrees. However, when applied to an image segmentation, the IFS cannot completely overcome the influence of noise. With the aim of performing noisy image segmentation under several criteria, this paper defines a noise robust IFS (NR-IFS) for an image and then presents a novel noise robust multiobjective evolutionary intuitionistic fuzzy clustering algorithm (NR-MOEIFC). A majority dominated suppressed similarity measure using the neighborhood statistics and the competitive learning is proposed to obtain the NR-IFS representation for the image corrupted by noise. Then, the NR-IFS is fully used to motivate the whole process of multiobjective evolutionary clustering: first, computing a three-parameter intuitionistic fuzzy distance measure; second, constructing intuitionistic fuzzy fitness functions; third, designing a nonuniform intuitionistic fuzzy mutation operator; and forth, defining an intuitionistic fuzzy cluster validity index to select the optimal solution from the final nondominated solution set. The histogram statistics of NR-IFS are adopted in the NR-MOEIFC to greatly reduce the computational complexity. Experimental results on Berkeley and real magnetic resonance images reveal that the NR-MOEIFC behaves well in noise robustness and segmentation performance while requiring a low time cost.

    更新日期:2019-02-06
  • Enhanced Predictor-Based Control Synthesis for Discrete-Time TS Fuzzy Descriptor Systems With Time-Varying Input Delays
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2018-11-19
    Antonio González; Thierry-Marie Guerra

    We present a novel predictor-feedback gain-scheduled control synthesis method for discrete-time Takagi–Sugeno (TS) fuzzy descriptor systems with unknown time-varying input delays. The goal is to reduce the computational complexity in the control design by keeping the descriptor form whilst improving the closed-loop robust performance. It is worth mentioning the following aspects: 1) our approach easily allows dealing with parametric uncertainties on the descriptor matrix, which are generally difficult to handle via matrix inversion; and 2) the control design problem is casted into linear matrix inequality conditions by using nonquadratic Lyapunov functions, the Projection Lemma, and small gain theory. Finally, three examples are provided to show the effectiveness of the proposed method.

    更新日期:2019-02-06
  • Introducing IEEE Collabratec
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-01

    "Advertisement, IEEE."

    更新日期:2019-02-06
  • IEEE Membership
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-01

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    更新日期:2019-02-06
  • IEEE Computational Intelligence Society
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-01

    "Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication."

    更新日期:2019-02-06
  • Adaptive Fuzzy Event-Triggered Control for Stochastic Nonlinear Systems with Full State Constraints and Actuator Faults
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-01
    Hui Ma; Hongyi Li; Hongjing Liang; Guowei Dong

    In this paper, an adaptive fuzzy output feedback control problem is investigated for a class of stochastic nonlinear systems, in which the fuzzy logic systems (FLSs) are adopted to approximate the unknown nonlinear functions. A reduced-order observer and a general fault model are designed to observe the unavailable state variables and describe the actuator faults, respectively. An event-triggered control law is developed to reduce the communication burden from the controller to the actuator. Meanwhile, the barrier Lyapunov functions (BLFs) are constructed to guarantee that all the states of the stochastic nonlinear system are not to violate their constraints. Furthermore, an observer-based adaptive fuzzy event-triggered control strategy is proposed for the full state constrained nonlinear system with actuator faults based on backstepping technique, which can guarantee that all the signals in the closed-loop system are bounded and the tracking error converges to a small neighborhood of the origin in a finite time. Finally, simulation results are given to illustrate the effectiveness of the proposed control scheme.

    更新日期:2019-02-06
  • Monotone Interval Fuzzy Inference Systems
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-01
    Yi Wen Kerk; Kai Meng Tay; Chee Peng Lim

    In this paper, we introduce the notion of a monotone fuzzy partition, which is useful for constructing a monotone zero-order Takagi-Sugeno-Kang Fuzzy Inference System (ZOTSK-FIS). It is known that a monotone ZOTSK-FIS model can always be produced when a consistent, complete, and monotone fuzzy rule base is used. However, such an ideal situation is not always available in practice, because a fuzzy rule base is susceptible to uncertainties, e.g., inconsistency, incompleteness, and non-monotonicity. As a result, we devise an interval method to model these uncertainties by considering the minimum interval of acceptability of a fuzzy rule, resulting in a set of monotone interval-valued fuzzy rules. This further leads to the formulation of a Monotone Interval Fuzzy Inference System (MIFIS) with a minimized uncertainty measure. The proposed MIFIS model is analyzed mathematically and evaluated empirically for the Failure Mode and Effect Analysis (FMEA) application. The results indicate that MIFIS outperforms ZOTSK-FIS, and allows effective decision making using uncertain fuzzy rules solicited from human experts in tackling real-world FMEA problems.

    更新日期:2019-02-06
  • Strong Laws of Large Numbers for IVM-events
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-01
    Piotr Nowak; Olgierd Hryniewicz

    Non-standard probability theories have been developed for modeling random systems in complex spaces, such as, e.g., quantum systems. One of these theories, the MV-algebraic probability theory, involves the notions of state and observable, which were introduced by abstracting the properties of the Kolmogorovian probability measure and the classical random variable, as well as the notion of independence. Although within these non-standard probability theories many important theorems, including the strong law of large numbers (SLLN) for sequences of independent and identically distributed observables, have been considered, some practical applications require their further development. This paper is devoted to the development of the IVM-probability theory for data described by interval-valued fuzzy random sets (IVM-events). The generalizations of Marcinkiewicz-Zygmund SLLN and Brunk-Prokhorov SLLN for independent IVM-events have been proved within this new theory. Our results open new possibilities in the theoretical analysis of imprecise random events in more complex spaces.

    更新日期:2019-02-06
  • Decentralized Event-Triggered H∞ Control for Affine Fuzzy Large-Scale Systems
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-01
    Huimin Wang; Guang-Hong Yang

    This paper studies the decentralized event-triggered (ET) controller design problem of nonlinear large-scale systems modeled by affine fuzzy systems. Based on the information of membership functions, the considered fuzzy system is represented by a multiple operating-regime-based model. To reduce the frequency of controller updates and save the limited network bandwidth, a novel ET transmission scheme is proposed. Then, a decentralized piecewise ET controller is constructed using sampled states. Combining with some convex optimization techniques, convex decentralized ET controller design conditions are derived, in which the unmatched regions induced by ET scheme are considered. In contrast to the existing results, the proposed control synthesis approach leads to less computational burden, and the assumption on bounds of membership functions has been removed. A numerical example is finally given to show the validity of the derived results.

    更新日期:2019-02-06
  • Fuzzy adaptive fault-tolerant control for uncertain nonlinear systems with unknown dead-zone and unmodeled dynamics
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-01
    Yan-Hui Jing; Guang-Hong Yang

    This paper investigates the problem of adaptive fault-tolerant tracking control for uncertain nonlinear systems subject to input dead-zone and full state constraints. Firstly, an error transformation approach is introduced to guarantee that all states do not violate their constraint bounds. Then, to avoid the issue of “explosion of complexity” in handling the derivative computation for the virtual control laws and improve the robust control performance, a novel nonlinear filter is proposed together with designing adaptive laws to compensate the bounded layer errors. In addition, the saturation function is employed to handle the difficulty caused by obtaining the explicit bounds for virtual signals at each step. By utilizing fuzzy logic systems to approximate unknown compound nonlinear functions, a novel fault-tolerant control scheme is proposed subject to online estimation technique. Finally, according to Lyapunov stability theory, it is concluded that all signals in the resulting closed-loop system are bounded and the tracking error satisfies the desired performance in presence of input dead-zone and actuator failures. Simulation results verify the effectiveness of the proposed control scheme.

    更新日期:2019-02-06
  • Hesitant Fuzzy Linguistic Consensus Model Based on Trust-Recommendation Mechanism for Hospital Expert Consultation
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-01
    Hangyao Wu; Peijia Ren; Zeshui Xu

    Aiming at validly improving the efficiency and quality of hospital expert consultation, a consensus model under uncertainty is established to operate hospital decision support system (HDSS). Considering that expert consultation is commonly used in emergency events associated with life rescue which exists uncertainty and complexity, the hesitant fuzzy linguistic terms are adopted. Additionally, since pairwise comparison information is efficient to express individual judgements, a novel concept of satisfaction degree towards hesitant fuzzy linguistic preference relation is utilized to measure the group consensus, a trust-recommendation mechanism is built to generate advice for experts, and a consensus model is constructed to handle HDSS in emergency decision-making problems. Some simulation experiments are designed to verify the availability and reasonability of the proposed model. Then, the proposed model is applied to address the selection of an emergency response plan for the influenza H7N9 virus, and finally, the strong robustness and powerful capability of the proposed model in the consensus reaching process is exhibited by making a comparative analysis among several recommendation mechanisms.

    更新日期:2019-02-06
  • On Distributive Laws of Uninorms Over Overlap and Grouping Functions
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-02-01
    Junsheng Qiao

    Overlap and grouping functions, as two new classes of special binary aggregation functions, have been investigated by several authors for applications in image processing, decision making, classification and fuzzy community detection problems. On the other hand, after Aczél studied the distributive law between two operations, the distributive laws among particular binary aggregation functions become an interesting and natural research area. In this paper, we continue to discuss this topic for uninorms and overlap and grouping functions. At first, we give some basic properties for the distributive law of uninorms over overlap functions. And then, we study this distributive law when the uninorms belong to one of the usual classes (e.g., $U_{min}, U_{max}$ , the family of idempotent uninorms, representable uninorms or uni- norms continuous on $]0,1[^2$ ). Finally, we investigate the distributive law of uninorms over grouping functions by an analogous way.

    更新日期:2019-02-06
  • Event-Triggered Adaptive Dynamic Programming Algorithm for Non-Zero-Sum Games of Unknown Nonlinear Systems via Generalized Fuzzy Hyperbolic Models
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-30
    Huaguang Zhang; Hanguang Su; Kun Zhang; Yanhong Luo

    In this paper, by incorporating the event-triggered mechanism and the adaptive dynamic programming (ADP) algorithm, a novel near-optimal control scheme for a class of unknown nonlinear continuous-time non-zero-sum (NZS) differential games is investigated. Firstly, a generalized fuzzy hyperbolic models (GFHM)-based identifier is established, using only the input-output data, to relax the requirement for the complete system dynamics. The convergence of the identifier weights and the asymptotic stability of the estimation error are also proved. Then under the event-based framework, the coupled Hamilton-Jacobi (HJ) equations are derived for the multiplayer NZS games. Then the adaptive critic design method is employed to approximate the optimal control policies, thus an identifier-critic architecture is developed to obtain the event-triggered controller. By the virtue of Lyapunov theory, a state-dependent triggering condition, which is different from the existing works, is developed to achieve the stability of the closed-loop control system both for the continuous and jump dynamics. The proposed method not only guarantees the uniformly ultimate boundedness of the system states, but also excludes the Zeno behavior. Finally, two numerical examples are simulated to substantiate the feasibility of the analytical design.

    更新日期:2019-01-31
  • Improving image matting by multiobjective evolutionary optimization based on fuzzy multi-criteria evaluation and decomposition
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-30
    Yihui Liang; Han Huang; Zhaoquan Cai; Zhifeng Hao

    Image matting is evolving for a wide range of applications including image/video editing. Sampling-based image matting aims to estimate the opacity of foreground objects by properly selecting a pair of foreground and background pixels for every unknown pixel. Sampling-based image matting is essentially an uncertain multi-criteria optimization problem (UMCOP). It shows unique advantages in parallelization and handling spatially disconnected regions. However, sampling-based approaches encounter difficulty in accurately evaluating pixel pairs and efficiently optimizing the large-scale UMCOP. To address these two problems, a fuzzy multi-criteria evaluation and a multiobjective evolutionary algorithm based on multi-criteria decomposition (MOEA-MCD) are proposed. We model three fuzzy membership functions for three selection criteria, and aggregate them by Einstein and averaging operators providing fuzzy multi-criteria evaluation (FMCE) for pixel pairs. MOEA-MCD uses the heuristic information for each criterion by multi-criteria decomposition that divides the single objective into multiple objectives and optimizes them simultaneously using a multiobjective optimizer with neighborhood grouping strategy. Experimental results show that FMCE accurately evaluate pixel pairs even in uncertain cases with low satisfaction degree of some evaluation criteria, and the heuristic information for each criterion enhances the population diversity of MOEA-MCD. MOEA-MCD outperforms state-of-the-art large-scale optimization approaches and sampling-based image matting approaches.

    更新日期:2019-01-31
  • Supervised Learning to Aggregate Data with the Sugeno Integral
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 
    Marek Gagolewski; Simon James; Gleb Beliakov

    The problem of learning symmetric capacities (or fuzzy measures) from data is investigated toward applications in data analysis and prediction as well as decision making. Theoretical results regarding the solution minimizing the mean absolute error are exploited to develop an exact branch-refine-and-bound-type algorithm for fitting Sugeno integrals (weighted lattice polynomial functions, max-min operators) with respect to symmetric capacities. The proposed method turns out to be particularly suitable for acting on ordinal data. In addition to providing a model that can be used for the general data regression task, the results can be used, among others, to calibrate generalized h-indices to bibliometric data.

    更新日期:2019-01-28
  • Observer-Based Sliding Mode Control for Uncertain Fuzzy Systems via Event-Triggered Strategy
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 
    Xinxin Liu; Xiaojie Su; Peng Shi; Chao Shen

    This paper investigates the problem of event-triggered control for a class of uncertain fuzzy time-delay systems based on sliding mode observer design. More specifically, the main purpose is to design an event-triggered mechanism by utilizing the information of system output and observer function. Based on the delay partitioning method and the Lyapunov-Krasovskii function approach, delay-dependent sufficient conditions are proposed to guarantee the overall system, including sliding mode dynamics and error dynamics, to be asymptotically stable with an Hinf performance. Furthermore, an event-triggered sliding mode controller is synthesized to ensure that the systems dynamics can be driven to the sliding region near the equilibrium point in finite time. Finally, a verification example is provided to demonstrate the feasibility and efficiency of the theoretical results presented.

    更新日期:2019-01-28
  • A Hybrid Cooperative Co-evolution Algorithm for Fuzzy Flexible Job Shop Scheduling
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-25
    Lu Sun; Lin Lin; Mitsuo Gen; Haojie Li

    Flexible scheduling is one of the most significant core techniques for intelligent manufacturing systems. Realization of an optimized schedule through flexible resources assignment is critical to the application and popularization of flexible scheduling, especially in uncertain manufacturing environments. In this paper, we consider flexible job shop scheduling with uncertain processing time represented by fuzzy numbers, which is named fuzzy flexible job shop scheduling. We propose an effective hybrid cooperative co-evolution algorithm (hCEA) for the minimization of fuzzy makespan. The hCEA combines particle swarm optimization with the genetic algorithm to improve the convergence ability. A parameter self-adaptive strategy is applied to the problems with different scale effectively as well. Five benchmarks and three large-scale problems with fuzzy processing time are adopted to test the hCEA. Computational results show that the hCEA performs better than the existing methods from the literature.

    更新日期:2019-01-28
  • Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-25
    Peng Xu; Zhaohong Deng; Chen Cui; Te Zhang; Kup-Sze Choi; Gu Suhang; Jun Wang; ShiTong Wang

    The superior interpretability and uncertainty modeling ability of Takagi-Sugeno-Kang fuzzy system (TSK FS) make it possible to describe complex nonlinear systems intuitively and efficiently. However, classical TSK FS usually adopts the whole feature space of the data for model construction, which can result in lengthy rules for high-dimensional data and lead to degeneration in interpretability. Furthermore, for highly nonlinear modeling task, it is usually necessary to use a large number of rules which further weakens the clarity and interpretability of TSK FS. To address these issues, a concise zero-order TSK FS construction method, called ESSC-SL-CTSK-FS, is proposed in this paper by integrating the techniques of enhanced soft subspace clustering (ESSC) and sparse learning (SL). In this method, ESSC is used to generate the antecedents and various sparse subspace for different fuzzy rules, whereas SL is used to optimize the consequent parameters of the fuzzy rules, based on which the number of fuzzy rules can be effectively reduced. Finally, the proposed ESSC-SL-CTSK-FS method is used to construct con-cise zero-order TSK FS that can explain the scenes in high-dimensional data modeling more clearly and easily. Experiments are conducted on various real-world datasets to confirm the advantages.

    更新日期:2019-01-28
  • An attitudinal trust recommendation mechanism to balance consensus and harmony in group decision making
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-25
    Jian Wu; Xue Li; Francisco Chiclana; Ronald R. Yager

    This article puts forward a trust based framework for building a recommendation mechanism for consensus in group decision making. To do that, it first presents an attitudinal trust model where experts assign trust weights to others considering the concept of attitude of the group. This approach allows for the implementation of the group attitude in a continuous scale ranging from a pessimistic attitude to an indifferent attitude. Thus, it can express the continuous trust status, and consequently it generalizes the traditional simplified trust model: ‘trusting’ and ‘distrusting’. In particular, three typical policies are defined as: ‘extreme trust policy’, ‘bounded trust policy’ and ‘indifferent trust policy’. Secondly, the attitudinal trust induced recommendation mechanism is established by a reasonable rule: the closer the experts, the higher their trust degree. This can guarantee that the consensus level of the inconsistent expert is increased after adopting the recommended advices. In addition to group consensus, experts envisage to keep their original opinions as much as possible. A harmony degree (HD) is defined to determine the extent of the difference between an original opinion and the corresponding revised opinion after adopting the recommended advices. Combining the HD index and the consensus index, a sensitivity analysis with attitudinal parameter is proposed to verify the rationality of the proposed attitudinal trust recommendation mechanism. In practice this will facilitate the inconsistent experts to achieve a balance between consensus degree and harmony degree by selecting an appropriate attitudinal parameter

    更新日期:2019-01-28
  • Observer-Based Fuzzy Adaptive Event-Triggered Control for Pure-Feedback Nonlinear Systems with Prescribed Performance
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-25
    Jianbin Qiu; Kangkang Sun; Tong Wang; Huijun Gao

    The paper studies the problem of fuzzy adaptive event-triggered control for a class of pure-feedback nonlinear systems, which contain unknown smooth functions and unmeasured states. Fuzzy logic systems (FLSs) are adopted to approximate unknown smooth functions and a fuzzy state observer is designed to estimate unmeasured states. Via the event-triggered control technique, the control signal of the fixed threshold strategy is obtained. By converting the tracking error into a new virtual error variable, an observer-based fuzzy adaptive event-triggered prescribed performance control strategy is designed. The key advantage is that the proposed method does not require the priori knowledge of partial derivatives of system functions, i.e., it relaxes the restrictive condition that the partial derivatives of system functions need to be known for pure-feedback nonlinear systems. Simulation results confirm the efficiency of the proposed method.

    更新日期:2019-01-28
  • Intuitionistic Fuzzy Twin Support Vector Machines
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-17
    Salim Rezvani; Xizhao Wang; Farhad Pourpanah

    Fuzzy twin support vector machine (FTSVM) is an effective machine learning technique which is able to overcome the negative impact of noise and outliers in tackling data classification problems. In FTSVM, the degree of membership function in the sample space just described the space between input data and class center, whilst ignored the position of input data in the feature space and simply miscalculated the ledge support vectors as noises. This paper presents an intuitionistic fuzzy twin support vector machine (IFTSVM) which combines the idea of intuitionistic fuzzy number with twin SVM (TSVM). An adequate fuzzy membership is employed to reduce the noise brought by the pollutant inputs. Two functions, i.e., linear and nonlinear, are used to formulate two non-parallel hyperplanes. IFTSVM not only reduces the influence of noises, it also distinguishes the noises from the support vectors. Further, this modification can minimize a newly formulated structural risk and improve the classification accuracy. Two artificial and eleven benchmark problems are employed to evaluate the effectiveness of the proposed IFTSVM model. To quantify the results statistically, the bootstrap technique with the 95% confidence intervals is used. The outcome shows that IFTSVM is able to produce promising results as compared with those from the original SVM, Fuzzy SVM (FSVM), FTSVM and other models reported in the literature.

    更新日期:2019-01-18
  • Model-based control and stability analysis of discrete-time polynomial fuzzy systems with time delay and positivity constraints
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-17
    Xiaomiao Li; Kamyar Merhan

    This paper proposes a novel Lyapunov stabilization analysis of discrete-time polynomial-fuzzy-model-based (PFMB) control systems with time delay under positivity constraint. The polynomial fuzzy model is constructed to describe the dynamics of a non-linear discrete-time system with time delay. A model-based polynomial fuzzy controller is designed using non-parallel distributed compensation (PDC) technique to stabilize the system while driving the system states to positive using the positivity constraints. The Lyapunov stability and positivity conditions are formulated as sum-of squares (SOS). To relax the conservativeness of the obtained stability results, two main methods are proposed in this paper: 1) the piecewise linear membership functions (PLMFs) is used to introduce the approximate error between piecewise and the original membership functions into the stability analysis, 2) introduce the boundary information of the premise variables into the stability analysis since the premise variables hold rich non-linearity information. A Numerical examples are given to demonstrate the effectiveness of the proposed approach.

    更新日期:2019-01-18
  • Bumpless Transfer Control for Switched Fuzzy Systems with $L_2$ -gain Property
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-16
    Ying Zhao; Jun Zhao; Jun Fu

    This study concentrates on the bumpless transfer control problem for a category of switched fuzzy systems with $L_2$ -gain property. A description of the bumpless transfer performance for switched fuzzy systems is introduced for the first time. A general multiple Lyapunov functions strategy is exploited to solve the bumpless transfer control problem of the switched fuzzy systems with $L_2$ -gain property. The multiple Lyapunov functions scheme allows the disconnection of the successive Lyapunov functions when a switching happens. A criterion on the bumpless transfer performance with the $L_2$ -gain property is established, allowing each subsystem satisfy neither the bumpless transfer performance nor the $L_2$ -gain property. Moreover, when only the $L_2$ -gain property is considered, the condition is mildly expressed in terms of matrix inequalities rather then the usual combination of matrix inequalities and matrix equalities. Finally, an example of controlling a mass-spring-damping model is offered to validate the effectiveness of the developed control strategy.

    更新日期:2019-01-17
  • Robust Sampled-data Fuzzy Control for Nonlinear Systems and Its Applications: Free-weight Matrix Method
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-16
    Gunasekaran Nallappan; Young-Hoon Joo

    In this paper, sampled-data control is proposed to stabilize the nonlinear system, which is expressed as a Takagi-Sugeno (T-S) fuzzy sub-models. Based on suitable Lyapunov-Krasovskii functional (LKF) along with new weighted integral inequalities, the sufficient conditions are derived in terms of linear matrix inequalities (LMIs), which ensure the exponential stability of the proposed closed loop T-S fuzzy system. The peculiarity of this paper is, considered as the novel integral inequalities and LKF are proposed which provides less conservatism on results when compared to existing results. Besides that, wind energy conversion systems, chaotic systems, and an inverted pendulum are validated with derived sufficient conditions. The corresponding simulation results show the merit of the proposed results over the existing works.

    更新日期:2019-01-17
  • Observer-based Sliding Mode Control for T-S Fuzzy Descriptor Systems with Time-delay
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-16
    Jiancheng Zhang; Fanglai Zhu; Hamid Reza Karimi; Fengning Wang

    This paper is concerned with the problem of observerbased sliding mode control designs for a class of descriptor T-S fuzzy systems with time-delay and uncertainties. Specifically, based on the detailed discussions on the existence conditions, a reducedorder robust observer is designed firstly where the influences of the uncertainties are totally removed. Secondly, by choosing appropriate coordinate transformations and matrix decompositions, an actual and a virtual sliding mode variables are constructed, and an observer-based sliding mode controller is developed to handle the uncertainties such that the virtual sliding mode surface can be reached and maintained in a finite time, while the actual sliding mode variable approaches to zeros asymptotically. And then, we prove that the system asymptotic stability can be guaranteed after the virtual sliding mode surface has been reached or the actual sliding mode variable approached to zero. In addition, the existence conditions for both the observer and the sliding mode controller are given in strict linear matrix inequility forms. Finally, a simulation example is given to demonstrate the effectiveness of the proposed method.

    更新日期:2019-01-17
  • Consistency-based Algorithms for Decision Making with Interval Fuzzy Preference Relations
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-16
    Fanyong Meng; Jie Tang; Hamido Fujita

    This paper reviews and analyses several consistency concepts for interval fuzzy preference relations (IFPRs). On the basis of the comparisons, one can find that Krejci's additive and multiplicative consistency concepts are more reasonable and flexible. Considering the issues that previous methods cannot fully address regarding incomplete and inconsistent IFPRs, this paper studies incomplete and inconsistent IFPRs using custom programming models based on Krejci's concepts. Meanwhile, programming models for judging the additive and multiplicative consistency of IFPRs are constructed, respectively. Considering the consensus of IFPRs in group decision making, a consensus index is defined, and programming models for improving the consensus levels of individual IFPRs are built. On the basis of the consistency and consensus analysis, two consistency-based algorithms for group decision making with inconsistent and incomplete IFPRs are offered. One method is based on Krejci's additive consistency concept, and the other uses Krejci's multiplicative consistency concept. Meanwhile, associated examples are provided to show the application of the new methods.

    更新日期:2019-01-17
  • Observer-based Adaptive Fuzzy Containment Control for Multiple Uncertain Nonlinear Systems
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-16
    Wei Wang; Shaocheng Tong

    The adaptive fuzzy containment control problem is addressed for multiple uncertain nonlinear strict-feedback systems with immeasurable states and multiple leaders under directed communication graphs. By utilizing fuzzy logic systems to model the followers' dynamics, a distributed fuzzy state observer is designed for the state estimation using only the relative position information. Then, an observer-based containment control scheme is constructed by the adaptive fuzzy control technique as well as the command filter. The filtering error loop is introduced to compensate the error arising from the command filter. The proposed adaptive fuzzy containment control scheme guarantees that all followers are driven into the dynamic convex hull spanned by the leaders with a bounded containment error, if there exists at least one of the leaders who has a directed path to the follower. Simulation results are given to illustrate the control performance of the proposed containment control method.

    更新日期:2019-01-17
  • Delayed Fuzzy Control of 1-D Reaction-Diffusion Equation Using Sampled-in-Space Sensing and Actuation
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-16
    Wen Kang; Dawei Ding

    This paper considers delayed fuzzy control of 1-D reaction-diffusion equation under distributed in-domain point actuation and measurements. The delay may be uncertain, but bounded by a known upper bound. We propose an observer-based controller that employs the averaged values of the observer. Sufficent conditions ensuring exponential stability of the closed-loop system are established in terms of Linear Matrix Inequalities (LMIs) by using the Lyapunov-Krasovskii method and the descriptor method. A numerical example demonstrates the efficiency of the results.

    更新日期:2019-01-17
  • Ordered Weighted Averaging Aggregation on Convex Poset
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-16
    LeSheng Jin; Radko Mesiar; Ronald R. Yager

    Ordered Weighted Averaging (OWA) operators, a family of aggregation functions, are widely used in human decision-making schemes to aggregate data inputs of a decision maker's choosing through a process known as OWA aggregation. The weight allocation mechanism of OWA aggregation employs the principle of linear ordering to order data inputs after the input variables have been rearranged. Thus, OWA operators generally cannot be used to aggregate a collection of n inputs obtained from any given convex partially ordered set (poset). This poses a problem since data inputs are often obtained from various convex posets in the real world. To address this problem, this paper proposes methods that practitioners can use in real-world applications to aggregate a collection of n inputs from any given convex poset. The paper also analyzes properties related to the proposed methods, such as monotonicity and weighted OWA aggregation on convex posets.

    更新日期:2019-01-17
  • Adaptive Fuzzy Fault-Tolerant Tracking Control for Partially Unknown Systems with Actuator Faults via Integral Reinforcement Learning Method
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-16
    Huaguang Zhang; Kun Zhang; Yuliang Cai; Jian Han

    In this paper, the fuzzy reinforcement learning based tracking control algorithm is first proposed for partially unknown systems with actuator faults. Based on Takagi-Sugeno fuzzy model, a novel fuzzy-augmented tracking dynamic is developed and the overall fuzzy control policy with corresponding performance index is designed, where four kinds of actuator faults, including actuator loss of effectiveness and bias fault, are considered. Combining the reinforcement learning technique and fuzzy-augmented model, the new fuzzy integral reinforcement learning based fault-tolerant control algorithm is designed and it runs in real time for the system with actuator faults. The dynamic matrices can be partially unknown and the online algorithm requires less information transmissions or computational load along with the learning process. Under the overall fuzzy fault-tolerant policy, the tracking objective is achieved and the stability is proved by Lyapunov theory. Finally, the applications in the single-link robot arm system and the complex pitch-rate control problem of F-16 fighter aircraft demonstrate the effectiveness of the proposed method.

    更新日期:2019-01-17
  • PALM: An Incremental Construction of Hyperplanes for Data Stream Regression
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-16
    Md Meftahul Ferdaus; Mahardhika Pratama; Sreenatha Anavatti; Matthew A. Garratt

    Data stream has been the underlying challenge in the age of big data because it calls for real-time data processing with the absence of a retraining process and/or an iterative learning approach. In realm of fuzzy system community, data stream is handled by algorithmic development of self-adaptive neurofuzzy systems (SANFS) characterized by the single-pass learning mode and the open structure property which enables effective handling of fast and rapidly changing natures of data streams. The underlying bottleneck of SANFSs lies in its design principle which involves a high number of free parameters (rule premise and rule consequent) to be adapted in the training process. This figure can even double in the case of type-2 fuzzy system. In this work, a novel SANFS, namely parsimonious learning machine (PALM), is proposed. PALM features utilization of a new type of fuzzy rule based on the concept of hyperplane clustering which significantly reduces the number of network parameters because it has no rule premise parameters. PALM is proposed in both type-1 and type-2 fuzzy systems where all of which characterize a fully dynamic rule-based system. That is, it is capable of automatically generating, merging and tuning the hyperplane-based fuzzy rule in the single pass manner. Moreover, an extension of PALM, namely recurrent PALM (rPALM), is proposed and adopts the concept of teacher-forcing mechanism in the deep learning literature. The efficacy of PALM has been evaluated through numerical study with six real-world and synthetic data streams from public database and our own real-world project of autonomous vehicles. The proposed model showcases significant improvements in terms of computational complexity and number of required parameters against several renowned SANFSs, while attaining comparable and often better predictive accuracy.

    更新日期:2019-01-17
  • A distributed delay method for event-triggered control of T-S fuzzy networked systems with transmission delay
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-16
    Shen Yan; Monquan Shen; Sing Kiong Nguang; Guangming Zhang; Liruo Zhang

    This article presents the event-triggered control for T-S fuzzy networked systems with transmission delay. An integral-based model is proposed for designing a new event triggered scheme, which relies on the mean of the system state and the last triggered state. To handle the asynchronous premises of fuzzy system and fuzzy controller, a novel triggering condition is added into the event-triggered mechanism. Then the closed loop T-S fuzzy event-triggered control system is established as a distributed delay system. With the help of the Legendre polynomials and their properties, the co-design conditions of triggering parameters and controller gains are given in linear matrix inequalities to ensure the asymptotic stability of the resulting closed-loop system. At last, an experiment via a practical wireless network is implemented to illustrate the effectiveness of the proposed approach.

    更新日期:2019-01-17
  • Stability and Stabilization of Takagi-Sugeno Fuzzy Systems with Hybrid Time-Varying Delays
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-16
    Yin Sheng; Frank Lewis; Zhigang Zeng; Tingwen Huang

    This paper investigates stability and stabilization of Takagi-Sugeno (T-S) fuzzy systems with discrete and distributed time-varying delays. First, $p$ th moment global exponential stability ( $p\geq 1$ ) of the addressed delayed fuzzy systems is considered by virtue of the comparison approach and inequality techniques. The developed algebraic criteria include some existing outcomes as special cases. Second, global exponential stabilization of the underlying delayed fuzzy systems is performed under a fuzzy state feedback controller. Third, considering that only a few studies have been dedicated to concerning finite-time stabilization of T-S fuzzy systems, by employing the comparison strategy and a nonlinear controller, finite-time stabilization of the nominated delayed fuzzy systems is presented. The obtained result herein establishes a general theoretical framework to analyze finite-time behavior of delayed T-S fuzzy systems. Finally, simulation examples are conducted to illustrate the validness of the results.

    更新日期:2019-01-17
  • Adaptive Fuzzy Containment Control of Nonlinear Strict-Feedback Systems with Full State Constraints
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-16
    Wei Wang; Shaocheng Tong

    In this paper, the distributed adaptive fuzzy containment control problem is investigated for a class of uncertain nonlinear systems guided by multiple dynamic leaders. Each follower is modeled by nonlinear strict-feedback systems subject to full state constraints. The function approximation technique using fuzzy logic systems is utilized to identify the unknown nonlinearities of each follower. Both the state feedback containment control and the observer-based output feedback containment control are constructed by combining distributed sliding-mode estimators with adaptive fuzzy backstepping control. To prevent constraint violation, multiple Barrier Lyapunov functions associated with error surfaces are introduced in the control design. It is proved that uniformly ultimately bounded containment control is achieved without violating full state constraints under the condition that for each follower, there exists at least one leader that has a directed path to that follower. Simulation studies are performed to show the effectiveness of the proposed theoretical results.

    更新日期:2019-01-17
  • On Matrix Norms, Stability and Stabilization of a Class of Discrete Takagi Sugeno Fuzzy Systems
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-16
    Khaled Belarbi

    Based on a result of stability for linear time varying systems, a sufficient condition for global stability, that does not use Lyapunov theory, is established for a class of discrete Takagi Sugeno, TS, fuzzy systems. The condition involves testing the norms of products of the matrices of the consequences. The approach seeks to determine if at an instant of time the system becomes a contraction mapping. Because the computation burden associated with these tests may become prohibitive, a necessary and sufficient condition for local stability is derived. In a way, this decreases the computational burden and allows to customize the test. A two-step iterative stabilization algorithm is then introduced. The problem involves norm minimization and an a posteriori stability test. Numerical examples are presented to demonstrate the applicability of the approach

    更新日期:2019-01-17
  • Fuzzy Super-Twisting Dynamic Surface Control for MIMO Strict Feedback Nonlinear Dynamic Systems with Super-Twisting Nonlinear Disturbance Observer and a New Partial Tracking Error Constraint
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-16
    Seong Ik Han

    This paper proposes a hybrid fuzzy dynamic surface control (DSC) method combined with a super-twisting control (STC) method and a super-twisting nonlinear disturbance observer (STO) for multi-input multi-output (MIMO) nonlinear strict-feedback systems is proposed, in addition to a new partial tracking error constraining method. For the design of the stabilizing controls of the DSC, the virtual tracking errors in recursive procedures were transformed into the design variables of the STC. The uncertainties were estimated by a super-twisting nonlinear disturbance observer, which includes the approximation error between the unknown plant dynamics and fuzzy optimization function, in addition to the external disturbances. Furthermore, the proposed DSC combined with the STC scheme can prevent the complex analysis problem of the filtered output error in conventional DSC schemes. Moreover, a novel convenient tracking error constraining method is also presented. Simulation results revealed that the proposed scheme has an improved tracking error performance and robustness to uncertainty than control systems with a conventional nonlinear disturbance observer and several other DSC schemes.

    更新日期:2019-01-17
  • Integrations of q-Rung Orthopair Fuzzy Continuous Information
    IEEE Trans. Fuzzy Syst. (IF 8.415) Pub Date : 2019-01-16
    Xiaoqin Shu; Zhenghai Ai; Zeshui Xu; Jianmei Ye

    Yager's q-rung orthopair fuzzy sets which extend Zadeh's fuzzy sets, use the membership and non-membership functions to describe things' vague characteristics, and the sum of the qth-power for the membership and non-membership functions is less than or equal to 1. More recently, some scholars have proposed a series of aggregation operators to fuse q-rung orthopair fuzzy discrete information. However, so far, there is no research on aggregating q-rung orthopair fuzzy continuous information. Thus, we proposed q-rung orthopair fuzzy definite integrals to fill this vacancy. First, we further study the operations of q-rung orthopair fuzzy numbers which are the core of q-rung orthopair fuzzy numbers, also, we introduce the limit of a q-rung orthopair fuzzy number sequence. Subsequently, we construct the q-rung orthopair fuzzy definite integrals step by step and give their concrete values, and discuss their integrability criteria from two perspectives. From the perspectives of modern analysis and the operational laws of q-rung orthopair fuzzy numbers, we investigate the q-rung orthopair fuzzy definite integrals in detail which is concise and considerably different from the investigative techniques of the previous research on aggregating continuous information. Finally, a practical example is provided to show the effectiveness, elasticity and superiority of the q-rung orthopair fuzzy definite integrals via comparing them with the existing methods.

    更新日期:2019-01-17
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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