Presents the table of contents for this issue of the publication.

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

Presents the table of contents for this issue of the publication.

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

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

Presents the table of contents for this issue of the publication.

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

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

Presents the table of contents for this issue of the publication.

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

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

Presents the table of contents for this issue of the publication.

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

The papers in this special section focus on cyborg intelligence. Well-known scientists and experts have expressed concern that robots may take over the world. More generally, there is a concern that robots could take over human jobs and leave billions of people suffering long-term unemployment. Yet, such concerns ignored the potential of intelligence techniques to enhance the natural capabilities of

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

In this sequel to our previous article (Cordero et al. , 2020) on general inference systems for reasoning with if–then dependencies, we study transformations of if–then rules to semantically equivalent collections of if–then rules suitable to solve several problems related to reasoning with data dependencies. We work in a framework of general lattice-based if–then rules whose semantics is parameterized

Network theory has a big role in driving fields like communication, e-commerce, and internet. There are several graph theoretical structures to represent natural and man-made networks. Stability and connectivity are two major factors associated with networks. A fuzzy graph is an effective representation for a weight normalized network. Different connectivity parameters in fuzzy graphs can be used to

The consistency of interval-valued fuzzy preference relations (IFPRs) is a prerequisite for the application of IFPRs in real problems. To support the application of IFPRs, various types of consistency of IFPRs have been developed. They all satisfy some fixed mathematical conditions under the assumption that decision makers are perfectly rational. In practice, this assumption is usually violated because

Numeric models (including fuzzy models) produce numeric results. There are no ideal models that deliver a complete match with the data. In this study, we advocate that a way of evaluating the quality of models can be realized at the higher level of abstraction by developing a concept of granular prediction. In this way, modeling results are expressed in the form of information granules, in particular

Classification algorithms have recently acquired great popularity due to their efficiency to generate models capable of solving high complexity problems. Specifically, black box models are the ones that offer the best results, since they greatly benefit from the enormous amount of data available to learn models in an increasingly accurate way. However, their main disadvantage compared to other simpler

This study is concerned with proposing much more effective Luenberger-like fuzzy observers than relevant multiinstant observers reported in recent literature, in other words, less conservative results can be given while less computing resource is consumed as an important premise. For the first time, the variation bound of two adjacent time-variant Lyapunov matrices encountered in the synthesis of fuzzy

Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this article, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the interclass overlaps in a natural way and better capture the underlying multimodel structure of data streams in the form

This article presents the observer-based event-triggered adaptive hybrid fuzzy dynamic surface control strategy for a category of uncertain nonstrict-feedback fractional-order nonlinear multi-input multi-output systems, including unknown time-varying delays and actuator faults. First, an adaptive hybrid fuzzy state observer and a serial-parallel estimation system are constructed to estimate the unmeasured

In this article, finite-time control is investigated for multiple time-delayed large-scale nonlinear systems against state and input constraints. There are multiple time-varying delays, intermittent actuator faults, and intermittent sensor faults in this model. Takagi–Sugeno fuzzy model is used to describe the nonlinear system. There are few tries to studying finite-time control for large-scale nonlinear

This article proposes a human posture classification system to classify the four postures of standing, bending, sitting, and lying using three-dimensional (3-D) fuzzy body voxel features obtained from two cameras and hierarchical fuzzy classifiers (FCs). The system consists of four parts: human body segmentation, 3-D body voxel model construction, feature extraction, and classification. A region-dependent

In real world, a typical decision-making problem in the medical field can be seen as an uncertain hesitant fuzzy multiattribute decision-making problem when existing experiences of decision-makers are insufficient. A three-way decision model is an effective tool to deal with uncertain decision-making problems, which can realize the classification of objects. However, different psychological behaviors

This article considers the problem of prescribed performance control for a class of affine nonlinear systems with state constraints. Fuzzy logic systems (FLSs) are employed to cope with unknown nonlinear functions. In addition, an additive transformation and one-to-one mapping method are introduced to handle the control problem of nonlinear systems with full-state constraints. Then, based on adaptive

Accessibility to smart devices provides opportunities for side-channel attacks (SCAs) on artificial intelligent (AI) models in the intelligent Internet of Things (IoT). However, the existing literature exposes some shortcomings: 1) incapability of quantifying and analyzing the leaked information through side channels of the intelligent IoT and 2) inability to devise efficient and accurate SCA algorithms

Time-series prediction based on information granule in which the algorithm is developed by deriving the relations existing in the granular time series, has achieved excellent success. However, the existing uncertainty in data and the computational demand of the granulation process make it difficult for these methods to accurately and efficiently achieve long-term prediction. In this article, a fuzzy-probabilistic

We propose four frameworks for clustering data with missing values. We first use a very simple method to impute the missing values and generate multiple imputed versions of the data. These views are then clustered together to obtain a common partition matrix and a common set of centroids. As the clustering framework, we use a multiview version of the Fuzzy-c-Means (MVFCM) and a multiview version of

Adverse drug–drug interaction (ADDI) causes harmful injuries and accidental deaths in patients, posing as a significant life-threatening issue in public health. Early prediction of ADDIs has become an increasingly concerning task for the safety of pharmacotherapy during clinical treatments. In this article, we propose an adaptive regularized multiattribute fuzzy distance (MAFD) learning model for ADDI

In this article, we study some topological properties of fuzzy quasi-normed spaces, and prove the open mapping and the closed graph theorems in the framework of fuzzy quasi-normed spaces. The obtained results are of important roles in developing fuzzy functional analysis and its applications.

This article explores a novel adaptive optimal control strategy for a class of sophisticated discrete-time nonlinear Markov jump systems (DTNMJSs) via Takagi–Sugeno fuzzy models and reinforcement learning (RL) techniques. First, the original nonlinear system model is represented by fuzzy approximation, while the relevant optimal control problem is equivalent to designing fuzzy controllers for linear

This article considers the problem of finite-time adaptive fuzzy switching event-triggered control for a class of nonaffine stochastic systems with periodic actuator faults and asymmetric error constraints. First, a framework of semiglobally finite-time stability in probability is established for stochastic systems. Then, unlike the existing observers, an adaptive state observer with faults compensation

In this article, the event-triggered cooperative output regulation problem of T–S fuzzy multiagent systems under jointly connected switching directed topologies is investigated. A distributed sampled-data event-triggered scheme is proposed such that the estimator, which estimates the exosystem, transmits the data information to its neighbors under the switching directed topologies at its triggering

This article investigates the fixed-time adaptive fuzzy containment control problem for nonlinear multiagent systems under the directed communication topologies. The controlled systems have the unknown internal dynamics and mismatched disturbances, and fuzzy logic systems are utilized to identify the unknown internal dynamics. The mismatched disturbances and approximate errors are reconstructed via

Subjective evaluation is a commonly used method in the real recognition process. Generally, two fuzziness can be found in evaluation information, namely what values should be given to fully describe the information and how to distinguish different values. The recently developed probabilistic hesitant fuzzy set could perfectly address these issues. In this article, we propose a dual-fuzzy convolutional

Large-scale multiagent reinforcement learning requires huge computation and space costs, and the too-long execution process makes it hard to train policies for agents. This work proposes a concept of fuzzy agent, which is a new paradigm for training homogeneous agents. Aiming at a lightweight and affordable reinforcement learning mechanism for large-scale homogeneous multiagent systems, we break the

One drawback of using the existing one-step forecasting models for long-term time series prediction is the cumulative errors caused by iterations. In order to overcome this shortcoming, this article proposes a trend-fuzzy-granulation-based adaptive fuzzy cognitive map (FCM) for long-term time series forecasting. Different from the original FCM-based forecasting models, a class of trend fuzzy information

Monotonic classification is a common task in the field of multicriteria decision-making, in which features and decision obey a monotonic constraint. The dominance-based rough set theory is an important mathematical tool for knowledge acquisition in monotonic classification tasks (MCTs). However, existing dominance-based rough set models are very sensitive to noise information, and only a misclassified

Real-world decision-making is based on human cognitive information, which is characterized by fuzziness and partial reliability. In order to better describe such information, Zadeh proposed the concept of Z-number. Ranking the Z-number is an indispensable step in solving the decision-making problem under the Z-number-based information. Golden rule representative value is a new concept introduced by

In this article, the fuzzy $\mathcal {H}_{\infty }$ sliding mode control problem for continuous-time switched nonlinear systems is investigated. The switching signal conforms to the persistent dwell-time switching mechanism. The first objective of this article is to construct a switched integral sliding surface that not only accommodates the switched nonlinear model, but ensures that the sliding mode

In this article, an adaptive fuzzy predefined time and accuracy tracking control scheme is proposed for strict-feedback nonlinear systems. A sufficient condition that can be used to determine whether the tracking error converges to a predefined region within a predefined time is first derived based on the definition of predefined time stability. Under the proposed stability criterion, an adaptive fuzzy

Uncertainties are widespread in real-world situations. In the existing studies, the uncertainties expressed by the agents engaged in a conflict situation have been extensively investigated. However, to what extent these agents can be trusted is often ignored and seldom taken into account. In this article, to manage these two sides of uncertainties, we propose a mathematical framework for multilevel

Liang et al. in 2000 defined interval type-2 fuzzy sets (IT2FSs), which constitute a subset of type-2 fuzzy sets. While the membership degrees in the former are functions from [0, 1] to [0, 1] (fuzzy truth values), the membership degrees in IT2FSs only take their values in $\lbrace {\text{0}},{\text{1}}\rbrace$ . Although all the initial work on IT2FSs involved convex membership degrees only, in 2015

Stability control for humanoid robots based on zero moment point (ZMP) control and impedance control are widespread. However, uncertain changes in the center of mass (CoM) height for ZMP control and specific regulation of the variable stiffness of impedance control have been challenging issues in previous studies. In this article, these two problems are solved by implementing fuzzy control-based regulations

Cyber-physical systems (CPS) consist of a variety of multicore architectures, including central processing units (CPU) and graphical processing units (GPU). In general, programmers assign sequential programs to the CPU while parallel applications are assigned to the GPU. This article provides a method for mapping an OpenCL application to a heterogeneous multicore architecture using active fuzzy learning

The encoder–decoder framework has been widely used in various practical artificial intelligence cyber-physical systems, including intelligent translation systems. The decoding process in such a framework usually demands the target-side representation, which is often learned by an autoaggressive decoder to simulate the target context information at the current time-step. However, the autoaggressive

In this article, a novel design of a hierarchicalfuzzy system (HFS) based on a self-organized fuzzy partition and fuzzy autoencoder is proposed. The initial rule set of the system is empty, and all the fuzzy sets and fuzzy rules are generated by a self-organized fuzzy partition algorithm. By adopting an improved box plot data standardization method, the processed data can more accurately represent

In this article, we investigate the saturation-tolerant prescribed control (SPC) for multiinput and multioutput nonlinear systems with unknown control directions and actuator faults. We propose a concise tunnel prescribed performance (TPP) with the control design independent of initial conditions and smaller overshoots achieved due to its tight feasible region. A novel auxiliary system, to tactfully

This article discusses the prescribed performance control problem for multiagent systems involving the state triggering and the controller output triggering simultaneously. To successfully apply the backstepping technique in the event-triggered control design, the virtual control signal is constructed by the original system state. Compared with the existing results on the prescribed performance, a

This article is concerned with the extended dissipative fuzzy proportional–integral–derivative (PID) control problem for nonlinear systems subject to controller parameter perturbations over a class of mixed fading channels. The sensors of plant are divided into two groups according to engineering practice, where the individual sensor group transmits the measurements to the controller via a respective

Consensus reaching process (CRP) is important and present in a wide range of application areas. In practical CRP, the managers (e.g., enterprise) often hire some informed individuals (e.g., persuaders) to promote the efficiency of consensus reaching. This article proposes a CRP with minimum cost of informed individuals and time constraint in large-scale group decision-making (LSGDM) with bounded confidence

The pervasive deployment of the Internet of Things (IoT) has significantly facilitated manufacturing and living. The diversity and continual updates of IoT systems make their security a crucial challenge, among which the detection of malicious network traffic turns out to be the most common yet destructive threat. Despite the efforts on feature engineering and classification backend designing, established

In this article, the sliding mode control problem is considered for interval type-2 fuzzy systems under the access-constrained communication network. The sensors and actuators are selected in a random manner, under which only a part of sensor/actuator nodes can be permitted to access the communication channels. The access status of the sensor and actuator is described by two independent Markov chains

Adaptive imagesteganography embeds secret messages into areas of cover images with complex features, including rich edges and complex textures. In this article, an adaptive image steganography technique based on the edge and complex texture areas of images is proposed, by comprehensively considering three rules in the design of image steganography. First, the embedding area is composed of the edge

Multiview clustering is an important topic in multiview learning, where the cooperation of different views is used to improve clustering performance. Although multiview clustering has made considerable progress, most existing methods only utilize the information of the original visible views, or only consider some hidden space information shared by different views. Two of the challenges are: 1) insufficient

This article considers the reachable set estimation problem and membership function dependent $H_\infty$ performance analysis for a class of fuzzy Markov jump systems (FMJSs) with mode-dependent time-varying delays and bounded external disturbances via sampled-data control. First, mode-dependent sampled-data control for the FMJS is designed using the Takagi–Sugeno (T–S) fuzzy method. Then, a novel

The finite-time dynamic event-triggered fuzzy output feedback fault-tolerant control problem is studied in this article for the interval type-2 (IT2) Takagi–Sugeno fuzzy system with parameter uncertainties and actuator faults. A fuzzy state observer is first developed to solve the immeasurable state problem. Second, by using the sampled estimating states and measured output signals, a dynamic event-triggered

In this article, an adaptive tracking control approach is developed for a class of strict-feedback nonlinear systems with time-varying full state constraints. As a breakthrough in this system, the special function constraints (whose constraint boundary is relevant to both state variables and time) are considered, which are rarely studied by research work. And there is no doubt that this method increases