<![CDATA[ IEEE Transactions on Systems, Man, and Cybernetics: Systems - new TOC ]]>
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TOC Alert for Publication# 6221021 2017July 24<![CDATA[Table of contents]]>478C11762469<![CDATA[IEEE Transactions on Systems, Man, and Cybernetics publication information]]>478C2C271<![CDATA[Energy Efficiency Prediction Based on PCA-FRBF Model: A Case Study of Ethylene Industries]]>${C}$ -means (FCM) algorithm integrated with principal component analysis (PCA) technology (PCA-FRBF). The PCA is used to denoise and reduce dimensions of data to decrease the training time and errors of the modeling process. The FCM is used to separate every fuzzy class in input space and decide the number of neurons in hidden layer to overcome the shortcoming of setting them by experience subjectively. Meanwhile, the robustness and effectiveness of the PCA-FRBF model are validated through the standard data set from the University of California Irvine repository. Moreover, to predict the energy efficiency of ethylene plants, a multi-inputs and single-output model of energy efficiency is established based on the PCA-FRBF for monthly data of ethylene production process. We obtain a rational allocation of crude oil, fuel, steam, water, and electricity, and the greatest benefit of ethylene plants under different technologies. Finally, the empirical results show the effectiveness and practicability of the PCA-FRBF model applied to predict and guide the ethylene production in the petrochemical industry.]]>478176317731921<![CDATA[Dissipativity Analysis and Synthesis for a Class of T–S Fuzzy Descriptor Systems]]>47817741784891<![CDATA[Determining Three-Way Decisions With Decision-Theoretic Rough Sets Using a Relative Value Approach]]>478178517992170<![CDATA[A Fuzzy Knowledge-Based Disassembly Process Planning System Based on Fuzzy Attributed and Timed Predicate/Transition Net]]>478180018132044<![CDATA[$H_\infty $ Disturbance Attenuation for Nonlinear Coupled Parabolic PDE–ODE Systems via Fuzzy-Model-Based Control Approach]]>${H_\infty }$ fuzzy control design is presented for the disturbance attenuation of a class of coupled systems described by a set of nonlinear ordinary differential equations (ODEs) and a semi-linear parabolic partial differential equation (PDE). The fuzzy control scheme consists of an ODE state feedback fuzzy subcontroller for the ODE subsystem and a PDE static output feedback fuzzy subcontroller for the PDE subsystem by using piecewise uniform actuators and pointwise sensors. Initially, the original nonlinear system is accurately represented by employing a Takagi–Sugeno fuzzy coupled parabolic PDE–ODE model. Then, an ${H_\infty }$ fuzzy controller is developed to exponentially stabilize the fuzzy coupled system while satisfying a prescribed ${H_\infty }$ performance of disturbance attenuation, whose existence condition is given by linear matrix inequalities. Finally, simulation results on a hypersonic rocket car are given to show the effectiveness of the proposed design method.]]>47818141825730<![CDATA[Robust Fuzzy-Model-Based Filtering for Nonlinear Cyber-Physical Systems With Multiple Stochastic Incomplete Measurements]]>$\boldsymbol {H_\infty }$ performance level. The filter gain parameters are determined by solving a convex optimization problem. Finally, the simulation study on the networked truck-trailer system is presented to show the effectiveness of the proposed estimator design.]]>47818261838679<![CDATA[Data Fusion and Type-2 Fuzzy Inference in Contextual Data Stream Monitoring]]>478183918531544<![CDATA[Disturbance Observer Based Composite Learning Fuzzy Control of Nonlinear Systems with Unknown Dead Zone]]>478185418621477<![CDATA[Neural Network Control of a Flexible Robotic Manipulator Using the Lumped Spring-Mass Model]]>478186318742009<![CDATA[Adaptive Neural Control of Stochastic Nonlinear Time-Delay Systems With Multiple Constraints]]>47818751883587<![CDATA[Finite Frequency $L_{2}{-}L_{\infty }$ Filtering of T-S Fuzzy Systems With Unknown Membership Functions]]>${L}_{{2}}{-}{L}_{\infty }$ filtering for Takagi-Sugeno fuzzy systems with unknown membership functions. An ${L}_{{2}}{-}{L}_{\infty }$ performance index is first defined in finite frequency domain from the signal’s point of view. By using matrix trace calculations, a new result based on ${L}_{{2}}{-}{L}_{\infty }$ performance analysis is derived in finite frequency range. Moreover, a design criterion of the desired finite frequency filter with varying gains is given in terms of a set of linear matrix inequalities and switching laws. It is shown that the proposed finite frequency filtering method can achieve better filtering performance than the existing full frequency ones. Finally, two simulation examples are introduced to verify the theoretical results.]]>478188418971033<![CDATA[Fuzzy Adaptive Fault-Tolerant Output Feedback Attitude-Tracking Control of Rigid Spacecraft]]>478189819082642<![CDATA[Adaptive Fault-Tolerant Control for Nonlinear System With Unknown Control Directions Based on Fuzzy Approximation]]>47819091918555<![CDATA[Guaranteed-Cost Finite-Time Fuzzy Control for Temperature-Constrained Nonlinear Coupled Heat-ODE Systems]]>47819191930665<![CDATA[$H_{\infty }$ Filtering for Continuous-Time T–S Fuzzy Systems With Partly Immeasurable Premise Variables]]>$H_{\infty }$ filtering problem for T–S fuzzy systems with partly immeasurable premise variables. By using measurable premise variables of fuzzy models as the premise variables of fuzzy filters, a new fuzzy filter scheme is constructed. Further based on the new filter scheme and a class of new line integral fuzzy Lyapunov functions, a convex condition for designing $H_{\infty }$ filters is proposed. In contrast to the existing approaches, the new condition can take full use of measurable premise variables for less conservative design. A numerical example is given to illustrate the effectiveness of the proposed method.]]>47819311940346<![CDATA[Fuzzy Observer-Based Fault Detection Design Approach for Nonlinear Processes]]>478194119521701<![CDATA[Local Synchronization Criteria of Markovian Nonlinearly Coupled Neural Networks With Uncertain and Partially Unknown Transition Rates]]>478195319641079<![CDATA[Fault Propagation Reasoning and Diagnosis for Computer Networks Using Cyclic Temporal Constraint Network Model]]>478196519781580<![CDATA[Adaptive Fuzzy Control of Nonlinear Systems With Unmodeled Dynamics and Input Saturation Using Small-Gain Approach]]>47819791989942<![CDATA[Prognosis and Health Monitoring of Nonlinear Systems Using a Hybrid Scheme Through Integration of PFs and Neural Networks]]>478199020042177<![CDATA[Imbalanced TSK Fuzzy Classifier by Cross-Class Bayesian Fuzzy Clustering and Imbalance Learning]]>478200520203723<![CDATA[Mental Models Analysis and Comparison Based on Fuzzy Rules: A Case Study of the Protests of June and July 2013 in Brazil]]>478202120332890<![CDATA[A New Design of $H$ -Infinity Piecewise Filtering for Discrete-Time Nonlinear Time-Varying Delay Systems via T–S Fuzzy Affine Models]]>$\boldsymbol {H}$ -infinity filter design for discrete-time state-delayed nonlinear systems. The nonlinear plant is expressed by a Takagi–Sugeno fuzzy-affine model and the state delay is considered to be time-varying with available lower and upper bounds. The purpose is to design an admissible filter that guarantees the asymptotic stability of the resulting filtering error system (FES) with a prescribed disturbance attenuation level in an $\boldsymbol {H}$ -infinity sense. By applying a new piecewise-fuzzy Lyapunov–Krasovskii functional, combined with a novel summation inequality, improved reciprocally convex inequality and $\boldsymbol {S}$ -procedure, the $\boldsymbol {H}$ -infinity performance analysis criterion is first developed for the FES. Furthermore, the filter synthesis is carried out by some elegant convexification techniques. Finally, simulation examples are employed to confirm the effectiveness and less conservatism of the proposed methods.]]>47820342047664<![CDATA[Adaptive Neural Control of Uncertain Nonstrict-Feedback Stochastic Nonlinear Systems with Output Constraint and Unknown Dead Zone]]>478204820591264<![CDATA[Neural Network Controller Design for an Uncertain Robot With Time-Varying Output Constraint]]>${n}$ -link robot is studied and the considered robot can be transformed as a class of multi-input–multioutput systems. The position of the robot or the output of the transformed systems is constrained in a time-varying compact set. It is commonly known that the constant constraint belongs to a special case of the time-varying constraint, and thus, it can be more general for handling practical problem as compared with the existing methods for robot. The neural approximation is used to estimate the unknown functions of systems and the time-varying barrier Lyapunov function is used to overcome the violation of constraints. It can prove the stability of the closed-loop systems by using Lyapunov analysis. The feasibility of the approach is demonstrated by performing a simulation example.]]>478206020681001<![CDATA[An Approach for Group Decision Making With Interval Fuzzy Preference Relations Based on Additive Consistency and Consensus Analysis]]>478206920821035<![CDATA[Extended Bonferroni Mean Under Intuitionistic Fuzzy Environment Based on a Strict t-Conorm]]>478208320991158<![CDATA[FNN Approximation-Based Active Dynamic Surface Control for Suppressing Chatter in Micro-Milling With Piezo-Actuators]]>478210021133176<![CDATA[Time-Varying Delay Compensation for a Class of Nonlinear Control Systems Over Network via $H_{\infty }$ Adaptive Fuzzy Controller]]>${H}_{{\infty }}$ adaptive fuzzy controller for a class of unknown nonlinear systems over network. There are two main problems in the networked control systems, the time-varying networked-induced delay and the data packet dropouts. The time-varying networked-induced delays cause degradation for the system performance that controlled over network and also the system can be unstable. Moreover, the delay problem is aggravated when packet losses occur during the transmission of data. The proposed controller has a filtered error to handle the networked-induced delays. Furthermore, it is robust to overcome some packet losses. The unknown nonlinear functions of the system are approximated using fuzzy logic systems. The robustness of the proposed controller is achieved by combining the adaptive fuzzy controller with ${H}_{{\infty }}$ control technique. The Lyapunov stability analysis is used to prove that the proposed controller is asymptotically stable. Stability of the whole closed loop system is guaranteed via the proposed controller in the presence of bounded external disturbance, data packet dropouts and time-varying networked-induced delays. An extensive example of an inverted pendulum system is studied in detail to verify the effectiveness of the proposed controller based on TrueTime toolbox with comparative results.]]>478211421241115<![CDATA[Teleoperation Control Based on Combination of Wave Variable and Neural Networks]]>478212521362300<![CDATA[Network-Based Fuzzy Control for Nonlinear Industrial Processes With Predictive Compensation Strategy]]>47821372147859<![CDATA[Passivity Analysis of Coupled Reaction-Diffusion Neural Networks With Dirichlet Boundary Conditions]]>478214821591010<![CDATA[Forecasting of Multivariate Time Series via Complex Fuzzy Logic]]>478216021711533<![CDATA[$H_{\infty }$ Fuzzy Fault Detection for Uncertain 2-D Systems Under Round-Robin Scheduling Protocol]]>${H}_{{\infty }}$ fault detection problem for a class of uncertain discrete-time nonlinear 2-D systems subject to Round-Robin scheduling protocol. The Takagi–Sugeno fuzzy model is used to approximate the nonlinearities, where the linear fractional uncertainties enter the system in a random way. A kind of widely used communication mechanism, namely, Round-Robin communication protocol, is adopted to periodically schedule the sensors and the fault detectors to realize the information exchange in order to reduce the bandwidth usage in a networked environment with limit resource. An improved 2-D fuzzy residual generator is constructed to detect the possible fault, where the stability analysis of the resulting augmented 2-D system is discussed. It is accomplished by using a combination of the basis-dependent Lyapunov-like function and the stochastic analysis techniques. Sufficient conditions are first established to guarantee the globally asymptotic stability of the error dynamics of the state estimation with prescribed ${H}_{{\infty }}$ performance constraints. Then, a residual generator is proposed to detect the possible faults. The effectiveness of the developed algorithm is demonstrated via application to the fault detection problem for a thermal process.]]>478217221841431<![CDATA[Adaptive Fuzzy Control of Stochastic Nonstrict-Feedback Nonlinear Systems With Input Saturation]]>478218521971443<![CDATA[Reliable Control of Fuzzy Systems With Quantization and Switched Actuator Failures]]>$ {l}_{ {2}}$ -$ {l}_{ {\infty }}$ performance index. Then, a numerical example is presented to demonstrate the effectiveness of the proposed new design method.]]>47821982208773<![CDATA[Adaptive Fuzzy Backstepping Control of Fractional-Order Nonlinear Systems]]>47822092217865<![CDATA[Fuzzy Approximator Based Adaptive Dynamic Surface Control for Unknown Time Delay Nonlinear Systems With Input Asymmetric Hysteresis Nonlinearities]]>${L}_{{\infty }}$ norm of the tracking error by using the initializing technique; 2) the assumptions on the time-delay functions are removed due to the use of the finite covering lemma and the FLSs; and 3) the proposed adaptive fuzzy dynamic surface control scheme can also compensate the asymmetric shifted Prandtl-Ishlinskii (ASPI) hysteresis without constructing the inverse of the ASPI model with the density function of ASPI model being unknown and estimated online to compensate the hysteresis. It is proved that all the signals in the closed-loop system are ultimately uniformly bounded and can be made arbitrarily small. Simulation results show the validity of the proposed method.]]>478221822322658<![CDATA[Partially Decoupled Image-Based Visual Servoing Using Different Sensitive Features]]>478223322431347<![CDATA[Dynamic Learning From Adaptive Neural Control of Robot Manipulators With Prescribed Performance]]>$ {n}$ -link robot manipulator subjected to unknown system dynamics and external disturbances. To achieve the prescribed performance, a performance function is introduced to describe the performance restrictions on tracking errors, and then specific performance requirements are served as a priori condition of tracking control design. By an error transformation method, the constrained tracking control problem of the original robot manipulator is transformed into the stabilization problem of an unconstrained augmented system. Then, a novel ANC scheme is proposed for the unconstrained system by combining a filter tracking error with radial basis function (RBF) neural network (NN) approximator, and all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. The external disturbances might make it difficult to achieve the accurate convergence of NN weight estimates. To overcome this difficulty, an appropriate state transformation is introduced to transform the closed-loop system into a linear time-varying system with small perturbed terms. Under partial persistent excitation condition of RBF NNs, the convergence of NN weight estimates is guaranteed, and then the experienced knowledge on the unknown robot manipulator dynamics can be stored with NN constant weights. Using the experienced knowledge, a static neural learning control is proposed to improve the system performances without time-consuming online parameter adjustment process, and the proposed learning control can also guarantee the prescribed transient and steady-state tracking control performance. Simulation results demonstrate the effectiveness of the proposed method.]]>478224422551494<![CDATA[Application of Fuzzy Cognitive Maps and Run-to-Run Control to a Decision Support System for Global Set-Point Determination]]>478225622671543<![CDATA[Adaptive Neural-Fuzzy Sliding-Mode Fault-Tolerant Control for Uncertain Nonlinear Systems]]>47822682278904<![CDATA[Multiple Mittag–Leffler Stability of Fractional-Order Recurrent Neural Networks]]>$ {\prod _{i=1}^{n}(2K_{i}+1)}$ equilibrium points ($ {K_{i}\geq 0}$ ) and the local Mittag–Leffler stability of $ {\prod _{i=1}^{n}(K_{i}+1)}$ equilibrium points of them by using the geometrical properties of activation functions and algebraic properties of nonsingular ${M}$ -matrix. In contrast with many existing results, the derived results cover both mono-stability and multistability, and the activation functions herein could be nonmonotonic and nonlinear in any open interval. In addition, three numerical examples are elaborated to substantiate the efficacy and characteristics of the theoretical results.]]>478227922881182<![CDATA[Fault-Tolerant Control of a Nonlinear System Based on Generalized Fuzzy Hyperbolic Model and Adaptive Disturbance Observer]]>478228923001157<![CDATA[Novel Discrete-Time Zhang Neural Network for Time-Varying Matrix Inversion]]>et al. developed a special type of recurrent neural networks called Zhang neural network (ZNN) with continuous-time and discrete-time forms for time-varying matrix inversion. In this paper, a novel discrete-time ZNN (DTZNN) model for time-varying matrix inversion is proposed and investigated. Specifically, a new numerical difference rule based on Taylor series expansion is established in this paper for first-order derivative approximation. Then, by exploiting this Taylor-type difference rule, the novel DTZNN model, which is a five-step iteration algorithm, is thus proposed for time-varying matrix inversion. Theoretical results are also presented for the proposed DTZNN model to show its excellent computational property. Comparative numerical results with three illustrative examples further substantiate the efficacy and superiority of the proposed DTZNN model for time-varying matrix inversion compared with previous DTZNN models.]]>47823012310893<![CDATA[A Novel Fuzzy Modeling Structure-Decomposed Fuzzy System]]>478231123171532<![CDATA[Long-Term Forecasting the Survival in Liver Transplantation Using Multilayer Perceptron Networks]]>478231823291839<![CDATA[Event-Triggered Nonsynchronized $\mathcal {H}_{\infty }$ Filtering for Discrete-Time T–S Fuzzy Systems Based on Piecewise Lyapunov Functions]]>${\mathcal {H}_{\infty }}$ event-triggered filter for a class of Takagi–Sugeno fuzzy systems. Based on the proposed communication strategy, only the measured outputs of the physical plant that violate a predefined triggering condition will win the right for transmission in the shared communication channel. Considering that the implementation of the filter may not be synchronized with the plant trajectories due to the asynchronous premise variables in network environment, a novel observer-based piecewise fuzzy filter is proposed. By adopting the idea of input delay method, the filtering error dynamics is reformulated as a new event-triggered piecewise fuzzy system. By applying a piecewise Lyapunov–Krasovskii functional and some techniques on matrix convexification, a method of event-triggered ${\mathcal {H}_{\infty }}$ piecewise filter design is developed for the filtering error system concerned to be asymptotically stable with a given disturbance attenuation level and reduced transmission rate. Moreover, a co-design algorithm to derive the filter gains and the event triggering parameters is proposed. Illustrative examples are finally given to show the effectiveness of the developed method.]]>47823302341940<![CDATA[Affine TS Fuzzy Model-Based Estimation and Control of Hindmarsh–Rose Neuronal Model]]>478234223501296<![CDATA[Neural Network-Based Model-Free Adaptive Fault-Tolerant Control for Discrete-Time Nonlinear Systems With Sensor Fault]]>478235123621154<![CDATA[A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments]]>478236323773060<![CDATA[Adaptive Neural Dynamic Surface Control of Pure-Feedback Nonlinear Systems With Full State Constraints and Dynamic Uncertainties]]>478237823871090<![CDATA[Fuzzy-Model-Based Nonfragile Guaranteed Cost Control of Nonlinear Markov Jump Systems]]>47823882397691<![CDATA[Haptic Identification by ELM-Controlled Uncertain Manipulator]]>478239824091107<![CDATA[Adaptive Neural Network-Based Tracking Control for Full-State Constrained Wheeled Mobile Robotic System]]>478241024191091<![CDATA[Adaptive Fuzzy Control for Nonlinear Networked Control Systems]]>478242024301253<![CDATA[IEEE Open Access]]>47824312431592<![CDATA[Introducing IEEE Collabratec]]>478243224322170<![CDATA[IEEE Systems, Man, and Cybernetics Society Information]]>478C3C354<![CDATA[IEEE Transactions on Human-Machine Systems information for authors]]>478C4C459