<![CDATA[ IEEE Transactions on Fuzzy Systems - new TOC ]]>
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TOC Alert for Publication# 91 2018May 21<![CDATA[Table of Contents]]>262C139454<![CDATA[IEEE Transactions on Fuzzy Systems]]>262C2C264<![CDATA[Adaptive Fuzzy Control for Pure-Feedback Nonlinear Systems With Nonaffine Functions Being Semibounded and Indifferentiable]]>2623954081019<![CDATA[Uncertain Statistical Inference Models With Imprecise Observations]]>262409415199<![CDATA[Distribution-Based Behavioral Distance for Nondeterministic Fuzzy Transition Systems]]>262416429394<![CDATA[The Structure and Citation Landscape of <sc>IEEE Transactions on Fuzzy Systems</sc> (1994–2015)]]>IEEE Transactions on Fuzzy Systems (TFS) is a reputable international journal in the research domain of computer science and engineering. This study surveys the TFS publications between 1994 and 2015 which are indexed in Web of Sciences. The purpose is to present a comprehensive overview of the main influencing factors that affect the journal and identify the theme and citation structures of TFS. First of all, the publication and general citation structure of the journal as well as the most cited articles are analyzed. Then, the TFS authorship and coauthorship are well investigated, the maps of author cocitation network and representative subnetwork of the TFS coauthorship are presented. Next, this study gives a global overview of TFS publications. The most influential and productive countries/territories as well as the temporal analysis of the leading countries/territories are presented. Finally, the cocitation network for exploring the ground-breaking research is generated and the main cocitation clusters of TFS publications are discovered.]]>262430442718<![CDATA[Human Reliability Analysis Based on Human Abilities Theory Model]]>262443453497<![CDATA[Observer-Based <inline-formula><tex-math notation="LaTeX">$H_{infty }$</tex-math></inline-formula> Sampled-Data Fuzzy Control for a Class of Nonlinear Parabolic PDE Systems]]>∞ sampled-data fuzzy control problem is addressed for a class of nonlinear parabolic partial differential equation (PDE) systems. With the aid of the modal decomposition technique, a nonlinear ordinary differential equation (ODE) model is initially derived to describe the dominant (slow) dynamics of the PDE system. Subsequently, the resulting nonlinear ODE model is accurately represented by the Takagi-Sugeno (T-S) fuzzy model. Then, based on the T- S fuzzy model, a finite-dimensional observer-based sampled-data fuzzy control design with H_{∞} performance is developed for the PDE system via employing a novel time-dependent functional. The outcome of the observer-based H_{∞} sampled-data fuzzy control problem can be formulated as a bilinear matrix inequality optimization problem. Moreover, an iterative optimization algorithm based on the linear matrix inequalities is given to obtain a suboptimal H_{∞} sampled-data fuzzy controller. Finally, simulation results on the Fisher equation and a temperature cooling fin of high-speed aerospace vehicle illustrate that the proposed design method is effective.]]>2624544732100<![CDATA[Adaptive Compensation for Infinite Number of Time-Varying Actuator Failures in Fuzzy Tracking Control of Uncertain Nonlinear Systems]]>2624744862761<![CDATA[Internal Fusion Functions]]>2624875032675<![CDATA[Fuzzy-Affine-Model-Based Memory Filter Design of Nonlinear Systems With Time-Varying Delay]]>∞ filtering problem for nonlinear systems with time-varying delay in a delay-dependent framework. The nonlinear plant is characterized by a continuous-time Takagi-Sugeno fuzzy-affine model with parametric uncertainties. The purpose is to develop a new approach for filter synthesis procedure with less conservatism. Specifically, by constructing a novel Lyapunov-Krasovskii functional, together with a Wirtinger-based integral inequality, reciprocally convex inequality and S-procedure, an improved criterion is first attained for analyzing the H_{∞} performance of the filtering error system, and then via some linearization techniques, the piecewise-affine memory filter synthesis is carried out. It is shown that the existence of desired filter gains can be explicitly determined by the solution of a convex optimization problem. Finally, simulation studies are presented to reveal the effectiveness and less conservatism of the developed approaches. It is anticipated that the proposed scheme can be further extended to the analysis and synthesis of continuous-time fuzzy-affine dynamic systems with integrated communication delays in the networked circumstance.]]>262504517724<![CDATA[Linear Model With Exact Inputs and Interval-Valued Fuzzy Outputs]]>262518530635<![CDATA[Adaptive Sliding Mode Control for Takagi–Sugeno Fuzzy Systems and Its Applications]]>2625315421615<![CDATA[New Mechanisms for Reasoning and Impacts Accumulation for Rule-Based Fuzzy Cognitive Maps]]>2625435551408<![CDATA[Negations With Respect to Admissible Orders in the Interval-Valued Fuzzy Set Theory]]>α operators. We introduce and discuss an approach to the construction of strong negations on intervals with respect to K_{α,β} orders based on an arbitrary couple of strong negations defined over the standard real interval [0, 1]. The introduced strong negations have a deep impact on all fields exploiting fuzzy methods dealing with intervals, allowing to introduce complements, dual aggregations, implications, entropies, etc.]]>262556568505<![CDATA[Observer-Based Adaptive Fuzzy Decentralized Optimal Control Design for Strict-Feedback Nonlinear Large-Scale Systems]]>262569584975<![CDATA[Switched Adaptive Fuzzy Tracking Control for a Class of Switched Nonlinear Systems Under Arbitrary Switching]]>2625855971546<![CDATA[A Belief-Theoretical Approach to Example-Based Pose Estimation]]>2625986113468<![CDATA[Fuzzy Double C-Means Clustering Based on Sparse Self-Representation]]>2626126262646<![CDATA[A Measure-Theoretic Foundation for Data Quality]]>262627639346<![CDATA[Cascaded Hidden Space Feature Mapping, Fuzzy Clustering, and Nonlinear Switching Regression on Large Datasets]]>262640655650<![CDATA[From Equilibrium-Based Business Intelligence to Information Conservational Quantum-Fuzzy Cryptography—A Cellular Transformation of Bipolar Fuzzy Sets to Quantum Intelligence Machinery]]>262656669801<![CDATA[Multiple Definite Integrals of Intuitionistic Fuzzy Calculus and Isomorphic Mappings]]>262670680666<![CDATA[Analysis of Data Generated From Multidimensional Type-1 and Type-2 Fuzzy Membership Functions]]>2626816931267<![CDATA[A Fuzzy Approach for Optimal Robust Control Design of an Automotive Electronic Throttle System]]>262694704762<![CDATA[Characterization of Uninorms With Continuous Underlying T-norm and T-conorm by Their Set of Discontinuity Points]]>262705714430<![CDATA[A Fast and Accurate Rule-Base Generation Method for Mamdani Fuzzy Systems]]>2627157331750<![CDATA[Feature Selection With Controlled Redundancy in a Fuzzy Rule Based Framework]]>2627347481508<![CDATA[Distributed Saturated Control for a Class of Semilinear PDE Systems: An SOS Approach]]>262749760651<![CDATA[Unknown Input Method Based Observer Synthesis for a Discrete Time Uncertain T–S Fuzzy System]]>262761770689<![CDATA[Spatial Filtering for EEG-Based Regression Problems in Brain–Computer Interface (BCI)]]>2627717811419<![CDATA[Sampled-Data Synchronization of Complex Networks With Partial Couplings and T–S Fuzzy Nodes]]>262782793749<![CDATA[Fuzzy Bag-of-Words Model for Document Representation]]>262794804491<![CDATA[Diagnostic Observer Design for T–S Fuzzy Systems: Application to Real-Time-Weighted Fault-Detection Approach]]>∞/L_{2} observer-based FD context. Meanwhile, the L_{-} fault sensitivity condition is addressed to optimize the fault detectability. Using fuzzy Lyapunov functions, sufficient conditions on the FD system design are studied. Two examples are given in the end to show the efficiency of the proposed results.]]>2628058161376<![CDATA[A Feature-Reduction Fuzzy Clustering Algorithm Based on Feature-Weighted Entropy]]>2628178351666<![CDATA[Adaptive Fuzzy Decentralized Control for a Class of Strong Interconnected Nonlinear Systems With Unmodeled Dynamics]]>262836846512<![CDATA[Granular Fuzzy Regression Domain Adaptation in Takagi–Sugeno Fuzzy Models]]>262847858528<![CDATA[Specificity Measures and Referential Success]]>262859868478<![CDATA[Tracking-Error-Based Universal Adaptive Fuzzy Control for Output Tracking of Nonlinear Systems with Completely Unknown Dynamics]]>2628698831273<![CDATA[Consensus Building for the Heterogeneous Large-Scale GDM With the Individual Concerns and Satisfactions]]>2628848981034<![CDATA[Path Tracking of an Autonomous Ground Vehicle With Different Payloads by Hierarchical Improved Fuzzy Dynamic Sliding-Mode Control]]>2628999141966<![CDATA[Multiobjective Programming for Type-2 Hierarchical Fuzzy Inference Trees]]>2629159361332<![CDATA[Neighbor Inconsistent Pair Selection for Attribute Reduction by Rough Set Approach]]>2629379501764<![CDATA[State Feedback Control for Interval Type-2 Fuzzy Systems With Time-Varying Delay and Unreliable Communication Links]]>262951966774<![CDATA[Modeling Self-Adaptive Software Systems by Fuzzy Rules and Petri Nets]]>2629679843438<![CDATA[Recurrent Mechanism and Impulse Noise Filter for Establishing ANFIS]]>2629859971028<![CDATA[A Three-Phase Method for Group Decision Making With Interval-Valued Intuitionistic Fuzzy Preference Relations]]>2629981010527<![CDATA[Filtering for Fuzzy Systems With Multiplicative Sensor Noises and Multidensity Quantizer]]>2 - I_{∞} filtering for discrete-time Takagi-Sugeno (T-S) fuzzy systems with multiplicative sensor noises over the channels with limited capacity. A more general multidensity logarithmic quantizer is designed to increase the utilization of the communication resources, and a sojourn-time-dependent Markov chain is used to model the variation of the quantizer density. Then, the fuzzy basis-, quantizer density-, and sojourn-time-dependent filter is designed for T-S fuzzy systems on the basis of the quantized measurements to improve the performance of the filter. Sufficient conditions are proposed to guarantee that the filtering error system is exponentially mean-square stable and achieves a prescribed I_{2} - I_{∞} performance. Finally, three examples are given to illustrate the developed new design techniques.]]>26210111022616<![CDATA[Multi-Criteria Decision Making with Interval Criteria Satisfactions Using the Golden Rule Representative Value]]>26210231031231<![CDATA[Inherent Fuzzy Entropy for the Improvement of EEG Complexity Evaluation]]>26210321035697<![CDATA[On Nie-Tan Operator and Type-Reduction of Interval Type-2 Fuzzy Sets]]>26210361039187<![CDATA[Further Results on Stabilization of Chaotic Systems Based on Fuzzy Memory Sampled-Data Control]]>26210401045372<![CDATA[Fault-Tolerant Controller Design for General Polynomial-Fuzzy-Model-Based Systems]]>i(x), i = 1,. . . , p has at least one zero row, is not required any more. A polynomial Lyapunov function candidate depending on any system state is applied to design the fault-tolerant controller, in which the number of rules and membership functions can be matched or mismatched with polynomial fuzzy model. To deal with nonconvex problem, some nonlinear terms are successfully described as an index to be optimized to zero by a semidefinite programming. Compared with some published works, the resultant sum of squares based design conditions are with less computation complexity and potentially more relaxed. Using the third-party MATLAB toolbox SOSTOOLS, simulation examples are given to illustrate the effectiveness of the new method.]]>26210461051448<![CDATA[Adaptive Output Fuzzy Fault Accommodation for a Class of Uncertain Nonlinear Systems With Multiple Time Delays]]>26210521057376<![CDATA[Gradual Complex Numbers and Their Application for Performance Evaluation Classifiers]]>26210581065512<![CDATA[Fuzzy Tracking Control for Switched Uncertain Nonlinear Systems With Unstable Inverse Dynamics]]>26210661072483<![CDATA[Ultra-Efficient Fuzzy Min/Max Circuits Based on Carbon Nanotube FETs]]>26210731078944<![CDATA[A Direct Approach for Determining the Switch Points in the Karnik–Mendel Algorithm]]>26210791085368<![CDATA[Comments on “Fuzzy-Model-Based Quantized Guaranteed Cost Control of Nonlinear Networked Systems”]]>26210861088126<![CDATA[IEEE Computational Intelligence Society]]>262C3C358<![CDATA[Information for authors]]>262C4C455