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Fuzzy Systems, IEEE Transactions on

Issue 2 • Date April 2013

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Displaying Results 1 - 23 of 23
  • Table of contents

    Page(s): C1
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  • IEEE Transactions on Fuzzy Systems publication information

    Page(s): C2
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  • Stationary Fuzzy Fokker–Planck Learning for Derivative-Free Optimization

    Page(s): 193 - 208
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (625 KB) |  | HTML iconHTML  

    Stationary fuzzy Fokker-Planck learning (SFFPL) is a recently introduced computational method that applies fuzzy modeling to solve optimization problems. This study develops a concept of applying SFFPL-based computations for nonlinear constrained optimization. We consider the development of SFFPL-based optimization algorithms which do not require derivatives of the objective function and of the constraints. The sequential penalty approach was used to handle the inequality constraints. It was proved under some standard assumptions that the carefully designed SFFPL-based algorithms converge asymptotically to the stationary points. The convergence proofs follow a simple mathematical approach and invoke mean-value theorem. The algorithms were evaluated on the test problems with the number of variables up to 50. The performance comparison of the proposed algorithms with some of the standard optimization algorithms further justifies our approach. The SFFPL-based optimization approach, due to its novelty, could possibly be extended to several research directions. View full abstract»

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  • Weighted Fuzzy Spiking Neural P Systems

    Page(s): 209 - 220
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (936 KB) |  | HTML iconHTML  

    Spiking neural P systems (SN P systems) are a new class of computing models inspired by the neurophysiological behavior of biological spiking neurons. In order to make SN P systems capable of representing and processing fuzzy and uncertain knowledge, we propose a new class of spiking neural P systems in this paper called weighted fuzzy spiking neural P systems (WFSN P systems). New elements, including fuzzy truth value, certain factor, weighted fuzzy logic, output weight, threshold, new firing rule, and two types of neurons, are added to the original definition of SN P systems. This allows WFSN P systems to adequately characterize the features of weighted fuzzy production rules in a fuzzy rule-based system. Furthermore, a weighted fuzzy backward reasoning algorithm, based on WFSN P systems, is developed, which can accomplish dynamic fuzzy reasoning of a rule-based system more flexibly and intelligently. In addition, we compare the proposed WFSN P systems with other knowledge representation methods, such as fuzzy production rule, conceptual graph, and Petri nets, to demonstrate the features and advantages of the proposed techniques. View full abstract»

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  • Fault Estimation and Tolerant Control for Fuzzy Stochastic Systems

    Page(s): 221 - 229
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4580 KB) |  | HTML iconHTML  

    This paper deals with the problem of fault estimation and fault-tolerant control for Takagi-Sugeno (T-S) fuzzy stochastic systems with sensor failures, where the system under consideration contains Itô-type stochastic disturbances. A new robust observer technique is presented to obtain the estimates of the system states and the sensor faults simultaneously, and a fuzzy fault-tolerant control scheme is developed to guarantee the closed-loop system to be exponentially stable in mean square. Sufficient conditions are obtained for the existence of admissible controllers, and it is shown that the reachability of the sliding-mode dynamics can be guaranteed under the proposed control techniques. Finally, a numerical example is provided to illustrate the effectiveness and applicability of the theoretic results obtained. View full abstract»

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  • Interval Type-2 Fuzzy Sets Constructed From Several Membership Functions: Application to the Fuzzy Thresholding Algorithm

    Page(s): 230 - 244
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1799 KB) |  | HTML iconHTML  

    An important problem in working with fuzzy sets is the correct construction of the membership functions that represent the objects of the system. Different experts construct different membership functions to represent the same object. In this paper, we construct an interval type-2 fuzzy set (IT2FS) with different fuzzy sets such that the length of the (membership) interval represents the uncertainty of the expert with respect to the choice of the membership function. We analyze this problem in the context of image segmentation. We propose a new version of the classical fuzzy thresholding algorithm, in which an expert can select multiple membership functions, to avoid the problem of selecting only one to represent the image. From these membership functions, we construct an IT2FS, and by minimizing its entropy, we find a threshold with which to binarize the image. We present experimental results that show that it is advisable to use this methodology when it is not known which membership function is the most suitable. View full abstract»

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  • Static-Output-Feedback {maths\cr H}_{bm \infty } Control of Continuous-Time T - S Fuzzy Affine Systems Via Piecewise Lyapunov Functions

    Page(s): 245 - 261
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1047 KB) |  | HTML iconHTML  

    This paper investigates the problem of robust H output feedback control for a class of continuous-time Takagi-Sugeno (T-S) fuzzy affine dynamic systems with parametric uncertainties and input constraints. The objective is to design a suitable constrained piecewise affine static output feedback controller, guaranteeing the asymptotic stability of the resulting closed-loop fuzzy control system with a prescribed H disturbance attenuation level. Based on a smooth piecewise quadratic Lyapunov function combined with S-procedure and some matrix inequality convexification techniques, some new results are developed for static output feedback controller synthesis of the underlying continuous-time T-S fuzzy affine systems. It is shown that the controller gains can be obtained by solving a set of linear matrix inequalities (LMIs). Finally, three examples are provided to illustrate the effectiveness of the proposed methods. View full abstract»

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  • Output Regulation of Polynomial-Fuzzy-Model-Based Control Systems

    Page(s): 262 - 274
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (920 KB) |  | HTML iconHTML  

    This paper investigates the output regulation problem of polynomial-fuzzy-model-based (PFMB) control systems with the consideration of system stability and control synthesis using the sum-of-squares (SOS)-based approach. A PFMB control system is formed by a polynomial fuzzy model and a polynomial fuzzy controller with integral action connected in a closed loop. Three cases of PFMB control systems regarding the premise membership functions and number of fuzzy rules, which have their own properties and advantages, are considered. The stability of the PFMB control systems is investigated based on the Lyapunov stability theory. With consideration of the information of membership functions, SOS-based stability conditions corresponding to each case are obtained to guarantee system stability and to realize output regulation. Simulation examples are given to compare the three cases and illustrate the effectiveness of the proposed approach. View full abstract»

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  • Adaptive Fuzzy Control via Observer Design for Uncertain Nonlinear Systems With Unmodeled Dynamics

    Page(s): 275 - 288
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (810 KB) |  | HTML iconHTML  

    In this paper, the problems of stability and tracking control for a class of large-scale nonlinear systems with unmodeled dynamics are addressed by designing the decentralized adaptive fuzzy output feedback approach. Because the dynamic surface control technique is introduced, the designed controllers can avoid the issue of “explosion of complexity,” which comes from the traditional backstepping design procedure that deals with large-scale nonlinear systems with unmodeled dynamics. In addition, a reduced-order observer is designed to estimate those immeasurable states. Based on the Lyapunov stability method, it is proven that all the signals in the closed-loop system are bounded, and the system outputs track the reference signals to a small neighborhood of the origin by choosing the design parameters appropriately. The simulation examples are given to verify the effectiveness of the proposed techniques. View full abstract»

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  • Fuzzy Logic System-Based Adaptive Fault-Tolerant Control for Near-Space Vehicle Attitude Dynamics With Actuator Faults

    Page(s): 289 - 300
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (450 KB) |  | HTML iconHTML  

    This paper addresses the problem of fault-tolerant control (FTC) for near-space vehicle (NSV) attitude dynamics with actuator faults, which is described by a Takagi-Sugeno (T-S) fuzzy model. First, a general actuator fault model that integrated varying bias and gain faults, which are assumed to be dependent on the system state, is proposed. Then, sliding mode observers (SMOs) are designed to provide a bank of residuals for fault detection and isolation. Based on Lyapunov stability theory, a novel fault diagnostic algorithm is proposed, which removes the classical assumption that the time derivative of the output error should be known. Further, for the two cases where the state is available or not, two accommodation schemes are proposed to compensate for the effect of the faults. These schemes do not need the condition that the bounds of the time derivative of the faults should be known. In addition, a sufficient condition for the existence of SMOs is derived according to Lyapunov stability theory. Finally, simulation results of NSV are presented to demonstrate the efficiency of the proposed FTC approach. View full abstract»

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  • Adaptive Output Feedback Control for Nonlinear Time-Delay Systems by Fuzzy Approximation Approach

    Page(s): 301 - 313
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1080 KB) |  | HTML iconHTML  

    In this paper, the problem of adaptive fuzzy tracking control via output feedback for a class of uncertain single-input single-output (SISO) strict-feedback nonlinear systems with unknown time-delay functions is investigated. Dynamic surface control technique is used to avoid the problem of “explosion of complexity,” which is caused by repeated differentiation of certain nonlinear functions in the backstepping design process. In addition, the fuzzy logic systems are utilized to approximate the unknown and desired control input signals directly instead of the unknown nonlinear functions. The designed controller can guarantee all the signals in the closed-loop system to be semiglobally uniformly ultimately bounded and the tracking error to converge to a small neighborhood of the origin. Simulations results are provided to demonstrate the effectiveness of the proposed methods. View full abstract»

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  • A Combined Backstepping and Stochastic Small-Gain Approach to Robust Adaptive Fuzzy Output Feedback Control

    Page(s): 314 - 327
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (977 KB) |  | HTML iconHTML  

    In this paper, an adaptive fuzzy output feedback control approach is investigated for a class of stochastic nonlinear strict-feedback systems without the requirement of states measurement. The stochastic nonlinear system addressed in this paper is assumed to possess unstructured uncertainties (unknown nonlinear functions) and, in the presence of unmodeled dynamics, dynamics disturbances. Fuzzy logic systems are used to approximate the unstructured uncertainties, and a fuzzy state observer is designed to estimate the unmeasured states. By combining the backstepping design technique with the stochastic small-gain approach, a new adaptive fuzzy output feedback control approach is developed. It is proved that the proposed control approach can guarantee that the closed-loop system is input-state-practically stability (ISpS) in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by appropriate choice of the design parameters. Simulation results are included to indicate that the proposed adaptive fuzzy control approach has a satisfactory control performance. In addition, the simulation comparisons with the previous methods show that the proposed adaptive fuzzy control approach has robustness to the dynamical uncertainties. View full abstract»

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  • Universal Fuzzy Models and Universal Fuzzy Controllers for Stochastic Nonaffine Nonlinear Systems

    Page(s): 328 - 341
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (652 KB) |  | HTML iconHTML  

    This paper investigates the universal fuzzy model and universal fuzzy controller problems for stochastic nonaffine nonlinear systems. The underlying mechanism of stochastic fuzzy logic is first discussed, and a stochastic generalized fuzzy model with new stochastic fuzzy rule base is then given. Based on their function approximation capability, these kinds of stochastic generalized fuzzy models are shown to be universal fuzzy models for stochastic nonaffine nonlinear systems under some sufficient conditions. An approach to stabilization controller design for stochastic nonaffine nonlinear systems is then developed through their stochastic generalized Takagi-Sugeno (T-S) fuzzy approximation models. Then, the results of universal fuzzy controllers for two classes of stochastic nonlinear systems, along with constructive procedures to obtain the universal fuzzy controllers, are also provided, respectively. Finally, a numerical example is presented to illustrate the effectiveness of the proposed approach. View full abstract»

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  • Intuitionistic Fuzzy Cognitive Maps

    Page(s): 342 - 354
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1313 KB) |  | HTML iconHTML  

    Uncertainty and imprecision characterize human cognitive and reasoning processes. Fuzzy cognitive maps (FCMs) are computationally simple yet effective structures to approximately model and simulate such processes. A limitation of current FCMs is that they are unable to model the hesitancy introduced into a complex system due to imperfect facts, missing information, and indecision. To cope with this issue, we propose a novel extension of the FCM model which is based on the theory of intuitionistic fuzzy sets. This intuitionistic FCM (iFCM) model, which is denoted as iFCM-II, inherently exploits the mathematical framework of intuitionistic fuzzy sets for the definition of the concepts constituting the cognitive map and their interrelations, as well as for reasoning. Furthermore, unlike the previous iFCM model, which is denoted as iFCM-I, it enables an intuitionistic estimation of hesitancy at the output concepts, thus offering a natural mechanism to assess the quality of its output. The advantages of the proposed iFCM model over the current FCM and iFCM models are demonstrated with reproducible numeric examples for process control and decision support applications. View full abstract»

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  • Parallel Distributed Hybrid Fuzzy GBML Models With Rule Set Migration and Training Data Rotation

    Page(s): 355 - 368
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1321 KB) |  | HTML iconHTML  

    We propose a parallel distributed model of a hybrid fuzzy genetics-based machine learning (GBML) algorithm to drastically decrease its computation time. Our hybrid algorithm has a Pittsburgh-style GBML framework where a rule set is coded as an individual. A Michigan-style rule-generation mechanism is used as a kind of local search. Our parallel distributed model is an island model where a population of individuals is divided into multiple islands. Training data are also divided into multiple subsets. The main feature of our model is that a different training data subset is assigned to each island. The assigned training data subsets are periodically rotated over the islands. The best rule set in each island also migrates periodically. We demonstrate through computational experiments that our model decreases the computation time of the hybrid fuzzy GBML algorithm by an order or two of magnitude using seven parallel processors without severely degrading the generalization ability of obtained fuzzy rule-based classifiers. We also examine the effects of the training data rotation and the rule set migration on the search ability of our model. View full abstract»

    Open Access
  • Differential Neuro-Fuzzy Controller for Uncertain Nonlinear Systems

    Page(s): 369 - 384
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1168 KB) |  | HTML iconHTML  

    In general, output-based controller design remains an important research area in control theory. Most of the existing solutions use a state estimation algorithm to reconstruct a plausible approximation of the real state. Then, one can apply a nonlinear controller, based on fuzzy logic, for example, to enforce the system trajectories to a desirable stable equilibrium point. Nevertheless, the aforementioned method may not be suitable for uncertain systems affected by external noises. State observers based on the system's structure cannot be applied in those cases. However, some sort of adaptive estimation may be developed. This paper deals with a fuzzy controller that was designed using the state observer solution when the dynamic model of a plant contains uncertainties or it is partially unknown. Differential neural network (DNN) approach is applied in this uninformative situation. A new learning law, containing an adaptive adjustment rate, is suggested to enforce the stability condition for the observer's free parameters. On the other hand, nominal weights are adjusted during the preliminary training process using the least mean square method. Lyapunov theory is used to obtain the upper bounds for the weight's dynamics. The proposed method seems to be a more advanced option to control uncertain systems when the state available information is reduced. Even when several options exist to control this class of nonlinear systems such as PID, the method introduced here uses the knowledge on the system behavior and enforces the reconstruction of the immeasurable states. This last issue is an extra advantage because it serves as a general software sensor. The well-known two-link manipulator is used to show the effectiveness of the proposed algorithm. A couple of cases are used here: the full actuated and the under-actuated systems. In both situations, the controller achieves a better performance than the well-known PID controllers and a fuzzy controller using the estima- ed states produced by a high-order sliding-mode observer. A practical example showing how the fuzzy controller based on the estimated states produced by the differential neural network observer is also presented. The system used to test the controller is the anaerobic digestion. In this case, the benefits of this output-based controller are also demonstrated. View full abstract»

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  • Switching-Type H Filter Design for T - S Fuzzy Systems With Unknown or Partially Unknown Membership Functions

    Page(s): 385 - 392
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (627 KB) |  | HTML iconHTML  

    This paper is concerned with the H filter design problem for Takagi-Sugeno (T-S) fuzzy systems with unknown or partially unknown membership functions. If the membership functions are allowed to be unknown or partially unknown, then a fuzzy system may describe a wide class of nonlinear systems. However, in this case, the filter design of fuzzy systems, which is based on parallel distributed compensator strategy, is infeasible. To tackle this difficulty, a switching mechanism, which depends on the lower and upper bounds of the unknown membership functions, is introduced to construct the H filter with varying gains. Some examples given in the simulation section show that the proposed method can achieve a better disturbance attenuation performance than a simple fixed-gain filter design approach. View full abstract»

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  • IEEE Transactions on Neural and Learning Systems: Special Issue on Learning in Non-(geo)metric Spaces [call for papers]

    Page(s): 393
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  • Special issue on web-based intelligence support systems using fuzzy set technology

    Page(s): 394
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  • Open Access

    Page(s): 395
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  • IEEE Global History Network [advertisement]

    Page(s): 396
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  • IEEE Computational Intelligence Society Information

    Page(s): C3
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  • IEEE Transactions on Fuzzy Systems information for authors

    Page(s): C4
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Aims & Scope

The IEEE Transactions on Fuzzy Systems (TFS) is published quarterly. TFS will consider papers that deal with the theory, design or an application of fuzzy systems ranging from hardware to software.

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Editor-in-Chief
Chin-Teng Lin
National Chiao-Tung University