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Fuzzy Systems, IEEE Transactions on

Issue 3 • Date June 2009

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Displaying Results 1 - 25 of 26
  • Table of contents

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

    Page(s): C2
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    Freely Available from IEEE
  • Developing a Fuzzy Bicluster Regression to Estimate Heat Tolerance in Plants by Chlorophyll Fluorescence

    Page(s): 485 - 504
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (963 KB) |  | HTML iconHTML  

    This paper presents a straightforward and useful fuzzy regression approach to estimate heat tolerance of plants by chlorophyll fluorescence measurement. The chlorophyll fluorescence measurement is an indicator of functional change of photosynthesis and is sensitive to temperature. Using the fluorescence-temperature curves, the experimenter may determine the heat tolerance (Tc) of plants by intersections of two linear regression lines. However, as traditional statistical regression analysis shows, the experiment may contain uncertain factors or phenomena such as leaf nature and growth environment, which concludes that data may vary among individual plants and different species. This research presents a fuzzy bicluster regression (FBCR) analysis with genetic algorithms, which helps derive a fuzzy intersection set and fuzzy heat tolerance of plants, in addition to the traditional statistical regression analysis. A fuzzy clustering concept and simultaneously optimal determination of data clusters is also developed. Especially, when there are nonlinear inflections in data curves, due to the imperative use of linear regression models, the traditional regression analysis may become unable to sufficiently model the uncertainties exhibited. The FBCR analysis can resolve this problem effectively due to the nonlinear tolerance of the system, even in a linear model. To demonstrate the FBCR analysis, it was applied to estimate the heat tolerance of five plant species. The results derived appeared to be more suitable than that of the conventional method. The approach may provide a useful means for the experimenters to derive more credible results from their chlorophyll fluorescence-temperature data. View full abstract»

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  • Factor Analysis Latent Subspace Modeling and Robust Fuzzy Clustering Using t -Distributions

    Page(s): 505 - 517
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (452 KB) |  | HTML iconHTML  

    Factor analysis is a latent subspace model commonly used for local dimensionality reduction tasks. Fuzzy c-means (FCM) type fuzzy clustering approaches are closely related to Gaussian mixture models (GMMs), and expectation-maximization (EM) like algorithms have been employed in fuzzy clustering with regularized objective functions. Student's t -mixture models (SMMs) have been proposed recently as an alternative to GMMs, resolving their outlier vulnerability problems. In this paper, we propose a novel FCM-type fuzzy clustering scheme providing two significant benefits when compared with the existing approaches. First, it provides a well-established observation space dimensionality reduction framework for fuzzy clustering algorithms based on factor analysis, allowing concurrent performance of fuzzy clustering and, within each cluster, local dimensionality reduction. Second, it exploits the outlier tolerance advantages of SMMs to provide a novel, soundly founded, nonheuristic, robust fuzzy clustering framework by introducing the effective means to incorporate the explicit assumption about student's t -distributed data into the fuzzy clustering procedure. This way, the proposed model yields a significant performance increase for the fuzzy clustering algorithm, as we experimentally demonstrate. View full abstract»

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  • Robust Quadratic-Optimal Control of TS-Fuzzy-Model-Based Dynamic Systems With Both Elemental Parametric Uncertainties and Norm-Bounded Approximation Error

    Page(s): 518 - 531
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (282 KB) |  | HTML iconHTML  

    This paper considers the design problem of the robust quadratic-optimal parallel-distributed-compensation (PDC) controllers for Takagi-Sugeno (TS) fuzzy-model-based control systems with both elemental parametric uncertainties and norm-bounded approximation error. By complementarily fusing the robust stabilizability condition, the orthogonal functions approach (OFA), and the hybrid Taguchi genetic algorithm (HTGA), an integrative method is presented in this paper to design the robust quadratic-optimal PDC controllers such that 1) the uncertain TS-fuzzy-model-based control systems can be robustly stabilized, and 2) a quadratic integral performance index for the nominal TS-fuzzy-model-based control systems can be minimized. In this paper, the robust stabilizability condition is proposed in terms of linear matrix inequalities (LMIs). By using the OFA and the LMI-based robust stabilizability condition, the robust quadratic-optimal PDC control problem for the uncertain TS-fuzzy-model-based dynamic systems is transformed into a static constrained-optimization problem represented by the algebraic equations with constraint of LMI-based robust stabilizability condition, thus greatly simplifying the robust optimal PDC control design problem. Then, for the static constrained-optimization problem, the HTGA is employed to find the robust quadratic-optimal PDC controllers of the uncertain TS-fuzzy-model-based control systems. Two design examples of the robust quadratic-optimal PDC controllers for an uncertain inverted pendulum system and an uncertain nonlinear mass-spring-damper mechanical system are given to demonstrate the applicability of the proposed integrative approach. View full abstract»

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  • Dual Fuzzy-Possibilistic Coclustering for Categorization of Documents

    Page(s): 532 - 543
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (406 KB) |  | HTML iconHTML  

    In this paper, we develop a new soft model dual fuzzy-possibilistic coclustering (DFPC) for document categorization. The proposed model targets robustness to outliers and richer representations of coclusters. DFPC is inspired by an existing algorithm called possibilistic fuzzy C-means (PFCM) that hybridizes fuzzy and possibilistic clustering. It has been shown that PFCM can perform effectively for low-dimensional data clustering. To achieve our goal, we expand this existing idea by introducing a novel PFCM-like coclustering model. The new algorithm DFPC preserves the desired properties of PFCM. In addition, as a coclustering algorithm, DFPC is more suitable for our intended high-dimensional application: document clustering. Besides, the coclustering mechanism enables DFPC to generate, together with document clusters, fuzzy-possibilistic word memberships. These word memberships, which are absent in the existing PFCM model, can play an important role in generating useful descriptions of document clusters. We detail the formulation of the proposed model and provide an extensive analytical study of the algorithm DFPC. Experiments on an artificial dataset and various benchmark document datasets demonstrate the effectiveness and potential of DFPC. View full abstract»

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  • H_{\infty } Controller Synthesis via Switched PDC Scheme for Discrete-Time T--S Fuzzy Systems

    Page(s): 544 - 555
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (669 KB) |  | HTML iconHTML  

    This paper is concerned with the problem of designing switched state feedback H infin controllers for discrete-time Takagi-Sugeno (T-S) fuzzy systems. New types of state feedback controllers, namely, switched parallel distributed compensation (PDC) controllers, are proposed, which are switched based on the values of membership functions. Switched quadratic Lyapunov functions are exploited to derive a new method for designing switched PDC controllers to guarantee the stability and H infin performances of closed-loop nonlinear systems. The design conditions are given in terms of solvability of a set of linear matrix inequalities. It is shown that the new method provides better or at least the same results of the existing design methods via the pure PDC scheme with a quadratic Lyapunov function or switched constant controller gain scheme. Numerical examples are given to illustrate the effectiveness of the proposed method. View full abstract»

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  • Improving Generalization of Fuzzy IF--THEN Rules by Maximizing Fuzzy Entropy

    Page(s): 556 - 567
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (271 KB) |  | HTML iconHTML  

    When fuzzy IF-THEN rules initially extracted from data have not a satisfying performance, we consider that the rules require refinement. Distinct from most existing rule-refinement approaches that are based on the further reduction of training error, this paper proposes a new rule-refinement scheme that is based on the maximization of fuzzy entropy on the training set. The new scheme, which is realized by solving a quadratic programming problem, is expected to have the advantages of improving the generalization capability of initial fuzzy IF-THEN rules and simultaneously overcoming the overfitting of refinement. Experimental results on a number of selected databases demonstrate the expected improvement of generalization capability and the prevention of overfitting by a comparison of both training and testing accuracy before and after the refinement. View full abstract»

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  • A Selection Model for Optimal Fuzzy Clustering Algorithm and Number of Clusters Based on Competitive Comprehensive Fuzzy Evaluation

    Page(s): 568 - 577
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (435 KB) |  | HTML iconHTML  

    Fuzzy c-means (FCM) and its variants suffer from two problems-local minima and cluster validity-which have a direct impact on the formation of final clustering. There are two strategies-optimization and center initialization strategies-that address the problem of local minima. This paper proposes a center initialization approach based on a minimum spanning tree to keep FCM from local minima. With regard to cluster validity, various strategies have been proposed. On the basis of the fuzzy cluster validity index, this paper proposes a selection model that combines multiple pairs of a fuzzy clustering algorithm and cluster validity index to identify the number of clusters and simultaneously selects the optimal fuzzy clustering for a dataset. The promising performance of the proposed center-initialization method and selection model is demonstrated by experiments on real datasets. View full abstract»

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  • Fuzzy Discrete-Event Systems Under Fuzzy Observability and a Test Algorithm

    Page(s): 578 - 589
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (286 KB) |  | HTML iconHTML  

    In order to more effectively cope with the real-world problems of vagueness, impreciseness, and subjectivity, fuzzy discrete-event systems (FDESs) were proposed recently. Notably, FDESs have been applied to biomedical control for human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS) treatment planning and sensory information processing for robotic control. Qiu independently developed supervisory control theory of FDESs. We note that the controllability of events in Qiu's work is fuzzy, but the observability of events is crisp, and the observability of events in Cao and Ying's work is also crisp, although the controllability is not completely crisp since the controllable events can be disabled with any degrees. Motivated by the necessity to consider the situation that the events may be observed or controlled with some membership degrees, in this paper, we establish the supervisory control theory of FDESs with partial observations in which both the observability and controllability of events are fuzzy instead. We formalize the notions of fuzzy controllability condition and fuzzy observability condition.In addition, controllability and observability theorem of FDESs is set up in a more generic framework. In particular, we present a detailed computing flow to verify whether the controllability and observability conditions hold. Thus, this result can decide the existence of supervisors. Also, we use this computing method to check the existence of supervisors in the controllability and observability theorem of classical discrete-event systems (DESs), which is a new method and different from classical case. A number of examples are elaborated on to illustrate the presented results. View full abstract»

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  • On the Distributivity of Fuzzy Implications Over Nilpotent or Strict Triangular Conorms

    Page(s): 590 - 603
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (269 KB) |  | HTML iconHTML  

    Recently, many works have appeared in this very journal dealing with the distributivity of fuzzy implications over t-norms and t-conorms. These equations have a very important role to play in efficient inferencing in approximate reasoning, especially fuzzy control systems. Of all the four equations considered, the equation I(x, S1 (y,z)) = S2(I(x,y),I(x,z)), when S1,S2 are both t-conorms and I is an R-implication obtained from a strict t-norm, was not solved. In this paper, we characterize functions I that satisfy the previous functional equation when S1,S2 are either both strict or nilpotent t-conorms. Using the obtained characterizations, we show that the previous equation does not hold when S1,S2 are either both strict or nilpotent t-conorms, and I is a continuous fuzzy implication. Moreover, the previous equation does not hold when I is an R -implication obtained from a strict t-norm, and S1,S2 are both strict t-conorms, while it holds for an R-implication I obtained from a strict t-norm T if and only if the t-conorms S1 = S2 are Phi-conjugate to the Lukasiewicz t-conorm for some increasing bijection phi of the unit interval, which is also a multiplicative generator of T. View full abstract»

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  • Delay-Dependent \hbox {H}_{\infty } Filter Design for Discrete-Time Fuzzy Systems With Time-Varying Delays

    Page(s): 604 - 616
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (322 KB) |  | HTML iconHTML  

    This paper investigates delay-dependent Hinfin filter design problems for discrete-time fuzzy systems with time-varying delays. First, a novel delay-dependent piecewise Lyapunov-Krasovskii functional (DDPLKF) is proposed in which both the upper bound of delays and the delay interval are considered. Based on this DDPLKF, the delay-dependent stability criteria for discrete-time systems with constant or time-varying delays are obtained, respectively. Then, delay-dependent full-order and reduced-order Hinfin filter design approaches are proposed. The filter parameters can be obtained by solving a set of linear matrix inequalities (LMIs). Simulation examples are also given to illustrate the performance of the proposed approaches. It is shown that our approaches are less conservative and that the corresponding Hinfin filters can achieve better performance than the existing approaches. View full abstract»

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  • Train Timetable Problem on a Single-Line Railway With Fuzzy Passenger Demand

    Page(s): 617 - 629
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (481 KB) |  | HTML iconHTML  

    The aim of the train timetable problem is to determine arrival and departure times at each station so that no collisions will happen between different trains and the resources can be utilized effectively. Due to uncertainty of real systems, train timetables have to be made under an uncertain environment under most circumstances. This paper mainly investigates a passenger train timetable problem with fuzzy passenger demand on a single-line railway in which two objectives, i.e., fuzzy total passengers' time and total delay time, are considered. As a result, an expected value goal-programming model is constructed for the problem. A branch-and-bound algorithm based on the fuzzy simulation is designed in order to obtain an optimal solution. Finally, some numerical experiments are given to show applications of the model and the algorithm. View full abstract»

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  • Real-Time Constrained Fuzzy Arithmetic

    Page(s): 630 - 640
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (466 KB) |  | HTML iconHTML  

    Klir introduced constrained fuzzy arithmetic (CFA) as a solution to the unnecessary precision loss when dealing with fuzzy quantities that represent linguistic variables. Since then, some attempts have been made to make CFA efficient, but none of these solutions is suitable for real-time applications. In this paper, we will propose a new CFA algorithm that can be used in such environments. View full abstract»

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  • Stability of Cascaded Fuzzy Systems and Observers

    Page(s): 641 - 653
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (639 KB) |  | HTML iconHTML  

    A large class of nonlinear systems can be well approximated by Takagi-Sugeno (TS) fuzzy models with linear or affine consequents. It is well known that the stability of these consequent models does not ensure the stability of the overall fuzzy system. Therefore, several stability conditions have been developed for TS fuzzy systems. We study a special class of nonlinear dynamic systems that can be decomposed into cascaded subsystems, which are represented as TS fuzzy models. We analyze the stability of the overall TS system based on the stability of the subsystems and prove that the stability of the subsystems implies the stability of the overall system. The main benefit of this approach is that it relaxes the conditions imposed when the system is globally analyzed, thereby solving some of the feasibility problems. Another benefit is that by using this approach, the dimension of the associated linear matrix inequality (LMI) problem can be reduced. For naturally distributed applications, such as multiagent systems, the construction and tuning of a centralized observer may not be feasible. Therefore, we also extend the cascaded approach to the observer design and use fuzzy observers to individually estimate the states of these subsystems. A theoretical proof of stability and simulation examples are presented. The results show that the distributed observer achieves the same performance as the centralized one, while leading to increased modularity, reduced complexity, lower computational costs, and easier tuning. Applications of such cascaded systems include multiagent systems, distributed process control, and hierarchical large-scale systems. View full abstract»

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  • On Constructing Parsimonious Type-2 Fuzzy Logic Systems via Influential Rule Selection

    Page(s): 654 - 667
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (465 KB) |  | HTML iconHTML  

    Type-2 fuzzy systems are increasing in popularity, and there are many examples of successful applications. While many techniques have been proposed for creating parsimonious type-1 fuzzy systems, there is a lack of such techniques for type-2 systems. The essential problem is to reduce the number of rules, while maintaining the system's approximation performance. In this paper, four novel indexes for ranking the relative contribution of type-2 fuzzy rules are proposed, which are termed R-values, c-values, omega1-values, and omega2-values. The R-values of type-2 fuzzy rules are obtained by applying a QR decomposition pivoting algorithm to the firing strength matrices of the trained fuzzy model. The c-values rank rules based on the effects of rule consequents, while the omega1-values and omega2-values consider both the rule-base structure (via firing strength matrices) and the output contribution of fuzzy rule consequents. Two procedures for utilizing these indexes in fuzzy rule selection (termed ldquoforward selectionrdquo and ldquobackward eliminationrdquo) are described. Experiments are presented which demonstrate that by using the proposed methodology, the most influential type-2 fuzzy rules can be effectively retained in order to construct parsimonious type-2 fuzzy models. View full abstract»

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  • Using an Efficient Immune Symbiotic Evolution Learning for Compensatory Neuro-Fuzzy Controller

    Page(s): 668 - 682
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (430 KB) |  | HTML iconHTML  

    This paper presents an efficient immune symbiotic evolution learning (ISEL) algorithm for the compensatory neuro-fuzzy controller (CNFC). The proposed ISEL method includes three major components-initial population, subgroup symbiotic evolution, and immune system algorithm. First, the self-clustering algorithm that determines proper input space partitioning and finds the mean and variance of the Gaussian membership functions and number of rules is applied to the initial population. Second, the subgroup symbiotic evolution method that uses each subantibody represents a single fuzzy rule and the evolution of the rule itself. Third, the immune system algorithm uses the clonal selection principle, such that antibodies between others of high similar degree are canceled, and these antibodies, after processing, will have higher quality, accelerating the search, and increasing the global search capacity. Finally, the proposed CNFC with ISEL (CNFC-ISEL) method is adopted to solve several nonlinear control problems. The simulation results have shown that the proposed CNFC-ISEL can outperform other methods. View full abstract»

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  • Evidential Reasoning Approach for Multiattribute Decision Analysis Under Both Fuzzy and Interval Uncertainty

    Page(s): 683 - 697
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (384 KB) |  | HTML iconHTML  

    Many multiple attribute decision analysis (MADA) problems are characterized by both quantitative and qualitative attributes with various types of uncertainties. Incompleteness (or ignorance) and vagueness (or fuzziness) are among the most common uncertainties in decision analysis. The evidential reasoning (ER) and the interval grade ER (IER) approaches have been developed in recent years to support the solution of MADA problems with interval uncertainties and local ignorance in decision analysis. In this paper, the ER approach is enhanced to deal with both interval uncertainty and fuzzy beliefs in assessing alternatives on an attribute. In this newly developed fuzzy IER (FIER) approach, local ignorance and grade fuzziness are modeled under the integrated framework of a distributed fuzzy belief structure, leading to a fuzzy belief decision matrix. A numerical example is provided to illustrate the detailed implementation process of the FIER approach and its validity and applicability. View full abstract»

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  • Neurofuzzy Networks With Nonlinear Quantum Learning

    Page(s): 698 - 710
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (294 KB) |  | HTML iconHTML  

    Nonlinear quantum processing allows the solution of an optimization problem by the exhaustive search on all its possible solutions. Hence, it can replace advantageously the algorithms for learning from a training set. In order to pursue this possibility in the case of neurofuzzy networks, we propose in this paper to tailor their architectures to the requirements of quantum processing. In particular, superposition is introduced to pursue parallelism and entanglement to associate the network performance with each solution present in the superposition. Two aspects of the proposed method are considered in detail: the binary structure of membership functions and fuzzy reasoning and the use of a particular nonlinear quantum algorithm for extracting the optimal neurofuzzy network by exhaustive search. View full abstract»

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  • Robust Fuzzy Observer-Based Fuzzy Control Design for Nonlinear Discrete-Time Systems With Persistent Bounded Disturbances

    Page(s): 711 - 723
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (263 KB) |  | HTML iconHTML  

    To date, nonlinear l infin- gain control problems have not been solved by the conventional control methods for nonlinear discrete-time systems with persistent bounded disturbances. This study introduces fuzzy observer-based fuzzy control design, where the premise variables depend on the state variables estimated by a fuzzy observer, to deal with the nonlinear l infin-gain control problem. The fuzzy control design for this case is more flexible but much more complex than that for the case where the premise variables depend on the state variables. First, the Takagi-Sugeno (T-S) fuzzy model is employed to represent the nonlinear discrete-time system. Next, based on the fuzzy model, a fuzzy observer-based fuzzy controller is developed to minimize the upper bound of l infin-gain of the closed-loop system under some nonlinear matrix inequality (non-LMI) constraints. A novel decoupled method is proposed in this study to transform the non-LMI conditions into some linear matrix inequality (LMI) forms. By the proposed decoupled method and the genetic algorithm, the l infin-gain fuzzy observer-based fuzzy control problem for the nonlinear discrete-time systems can be easily solved by an LMI-based optimization method. The proposed methods, which efficiently attenuate the peak of perturbation due to persistent bounded disturbances, extend the l infin-gain control problems from linear discrete-time systems to nonlinear discrete-time systems. A simulation example is given to illustrate the design procedures and to confirm the l infin-gain performance of the proposed method. View full abstract»

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  • Control Law Proposition for the Stabilization of Discrete Takagi–Sugeno Models

    Page(s): 724 - 731
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (238 KB) |  | HTML iconHTML  

    This paper deals with the stabilization of a class of discrete nonlinear models, namely those in the Takagi-Sugeno form; its main goal is to reduce conservatism of existing stabilization conditions using a special class of candidate Lyapunov functions and an enhanced control law. It is shown that the use of the aforementioned Lyapunov function leads to less-pessimistic solutions. The usefulness of the new control law is shown through several examples. View full abstract»

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  • Comments on "Adaptive Fuzzy H_{\infty } Stabilization for Strict-Feedback Canonical Nonlinear Systems Via Backstepping and Small-Gain Approach

    Page(s): 732 - 733
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (58 KB)  

    "For original paper see Y. S. Yang, C. J. Zhou, ibid., vol. 13, no. 1, p. 104-114, (2005)". In this note, we point out some mistakes in a 2005 paper by Yang and Zhou. View full abstract»

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  • IEEE Transactions on Autonomous Mental Development call for papers

    Page(s): 734
    Save to Project icon | Request Permissions | PDF file iconPDF (525 KB)  
    Freely Available from IEEE
  • IEEE copyright form

    Page(s): 735 - 736
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    Freely Available from IEEE
  • IEEE Computational Intelligence Society Information

    Page(s): C3
    Save to Project icon | Request Permissions | PDF file iconPDF (37 KB)  
    Freely Available from IEEE

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.

Full Aims & Scope

Meet Our Editors

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