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Fuzzy Systems, IEEE Transactions on

Issue 6 • Date Dec. 2008

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  • Table of contents

    Publication Year: 2008 , Page(s): C1 - 1389
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    Freely Available from IEEE
  • IEEE Transactions on Fuzzy Systems publication information

    Publication Year: 2008 , Page(s): C2
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    Freely Available from IEEE
  • Guest Editorial Evolving Fuzzy Systems–-Preface to the Special Section

    Publication Year: 2008 , Page(s): 1390 - 1392
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (43 KB)  

    First Page of the Article
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  • FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models

    Publication Year: 2008 , Page(s): 1393 - 1410
    Cited by:  Papers (42)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (719 KB) |  | HTML iconHTML  

    In this paper, we introduce a new algorithm for incremental learning of a specific form of Takagi-Sugeno fuzzy systems proposed by Wang and Mendel in 1992. The new data-driven online learning approach includes not only the adaptation of linear parameters appearing in the rule consequents, but also the incremental learning of premise parameters appearing in the membership functions (fuzzy sets), together with a rule learning strategy in sample mode. A modified version of vector quantization is exploited for rule evolution and an incremental learning of the rules' premise parts. The modifications include an automatic generation of new clusters based on the nature, distribution, and quality of new data and an alternative strategy for selecting the winning cluster (rule) in each incremental learning step. Antecedent and consequent learning are connected in a stable manner, meaning that a convergence toward the optimal parameter set in the least-squares sense can be achieved. An evaluation and a comparison to conventional batch methods based on static and dynamic process models are presented for high-dimensional data recorded at engine test benches and at rolling mills. For the latter, the obtained data-driven fuzzy models are even compared with an analytical physical model. Furthermore, a comparison with other evolving fuzzy systems approaches is carried out based on nonlinear dynamic system identification tasks and a three-input nonlinear function approximation example. View full abstract»

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  • A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning

    Publication Year: 2008 , Page(s): 1411 - 1424
    Cited by:  Papers (60)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (719 KB) |  | HTML iconHTML  

    This paper proposes a self-evolving interval type-2 fuzzy neural network (SEIT2FNN) with online structure and parameter learning. The antecedent parts in each fuzzy rule of the SEIT2FNN are interval type-2 fuzzy sets and the fuzzy rules are of the Takagi-Sugeno-Kang (TSK) type. The initial rule base in the SEIT2FNN is empty, and the online clustering method is proposed to generate fuzzy rules that flexibly partition the input space. To avoid generating highly overlapping fuzzy sets in each input variable, an efficient fuzzy set reduction method is also proposed. This method independently determines whether a corresponding fuzzy set should be generated in each input variable when a new fuzzy rule is generated. For parameter learning, the consequent part parameters are tuned by the rule-ordered Kalman filter algorithm for high-accuracy learning performance. Detailed learning equations on applying the rule-ordered Kalman filter algorithm to the SEIT2FNN consequent part learning, with rules being generated online, are derived. The antecedent part parameters are learned by gradient descent algorithms. The SEIT2FNN is applied to simulations on nonlinear plant modeling, adaptive noise cancellation, and chaotic signal prediction. Comparisons with other type-1 and type-2 fuzzy systems in these examples verify the performance of the SEIT2FNN. View full abstract»

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  • Incremental Evolution of Fuzzy Grammar Fragments to Enhance Instance Matching and Text Mining

    Publication Year: 2008 , Page(s): 1425 - 1438
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (609 KB) |  | HTML iconHTML  

    In many applications, it is useful to extract structured data from sections of unstructured text. A common approach is to use pattern matching (e.g., regular expressions) or more general grammar-based techniques. In cases where exact templates or grammar fragments are not known, it is possible to use machine learning approaches, based on words or n-grams, to identify the structured data. This is generally a two-stage (train/use) process that cannot easily cope with incremental extensions of the training set. In this paper, we combine a fuzzy grammar-based approach with incremental learning. This enables a set of grammar fragments to evolve incrementally, each time a new example is given, while guaranteeing that it can parse previously seen examples. We propose a novel measure of overlap between fuzzy grammar fragments that can also be used to determine the degree to which a string is parsed by a grammar fragment. This measure of overlap allows us to compare the range of two fuzzy grammar fragments (i.e., to estimate and compare the sets of strings that fuzzily conform to each grammar) without explicitly parsing any strings. A simple application shows the method's validity. View full abstract»

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  • An Evolving Fuzzy Predictor for Industrial Applications

    Publication Year: 2008 , Page(s): 1439 - 1449
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (909 KB) |  | HTML iconHTML  

    A reliable and online predictor is very useful to a wide array of industries to forecast the behavior of time-varying dynamic systems. In this paper, an evolving fuzzy system (EFS) is developed for system state forecasting. An evolving clustering algorithm is proposed for cluster generation. Clusters are established and modified based on constraint criteria of mapping consistence and compatible measurement. A novel recursive Levenberg-Marquardt (R-LM) method is proposed for online training of nonlinear EFS parameters. The viability of the developed EFS predictor is evaluated based on both simulation from benchmark data and real-time tests corresponding to machinery condition monitoring and material property testing. Test results show that the developed EFS predictor is an effective and accurate forecasting tool. It can capture the system's dynamic behavior quickly and track the system's characteristics accurately. The proposed clustering algorithm is an effective structure identification method. The recursive training technique is computationally efficient, and can effectively improve reasoning convergence. View full abstract»

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  • Fully Evolvable Optimal Neurofuzzy Controller Using Adaptive Critic Designs

    Publication Year: 2008 , Page(s): 1450 - 1461
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (348 KB) |  | HTML iconHTML  

    A near-optimal neurofuzzy external controller is designed in this paper for a static compensator (STATCOM) in a multimachine power system. The controller provides an auxiliary reference signal for the STATCOM in such a way that it improves the damping of the rotor speed deviations of its neighboring generators. A zero-order Takagi-Sugeno fuzzy rule base constitutes the core of the controller. A heuristic dynamic programming (HDP) based approach is used to further train the controller and enable it to provide nonlinear near-optimal control at different operating conditions of the power system. Based on the connectionist systems theory, the parameters of the neurofuzzy controller, including the membership functions, undergo training. Simulation results are provided that compare the performance of the neurofuzzy controller with and without updating the fuzzy set parameters. Simulation results indicate that updating the membership functions can noticeably improve the performance of the controller and reduce the size of the STATCOM, which leads to lower capital investment. View full abstract»

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  • Evolving Fuzzy-Rule-Based Classifiers From Data Streams

    Publication Year: 2008 , Page(s): 1462 - 1475
    Cited by:  Papers (51)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (776 KB) |  | HTML iconHTML  

    A new approach to the online classification of streaming data is introduced in this paper. It is based on a self-developing (evolving) fuzzy-rule-based (FRB) classifier system of Takagi-Sugeno ( eTS) type. The proposed approach, called eClass (evolving class ifier), includes different architectures and online learning methods. The family of alternative architectures includes: 1) eClass0, with the classifier consequents representing class label and 2) the newly proposed method for regression over the features using a first-order eTS fuzzy classifier, eClass1. An important property of eClass is that it can start learning ldquofrom scratch.rdquo Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for eClass (the number may grow, with new class labels being added by the online learning process). In the event that an initial FRB exists, eClass can evolve/develop it further based on the newly arrived data. The proposed approach addresses the practical problems of the classification of streaming data (video, speech, sensory data generated from robotic, advanced industrial applications, financial and retail chain transactions, intruder detection, etc.). It has been successfully tested on a number of benchmark problems as well as on data from an intrusion detection data stream to produce a comparison with the established approaches. The results demonstrate that a flexible (with evolving structure) FRB classifier can be generated online from streaming data achieving high classification rates and using limited computational resources. View full abstract»

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  • Efficient Self-Evolving Evolutionary Learning for Neurofuzzy Inference Systems

    Publication Year: 2008 , Page(s): 1476 - 1490
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (635 KB) |  | HTML iconHTML  

    This study proposes an efficient self-evolving evolutionary learning algorithm (SEELA) for neurofuzzy inference systems (NFISs). The major feature of the proposed SEELA is that it is based on evolutionary algorithms that can determine the number of fuzzy rules and adjust the NFIS parameters. The SEELA consists of structure learning and parameter learning. The structure learning attempts to determine the number of fuzzy rules. A subgroup symbiotic evolution is adopted to yield several variable fuzzy systems, and an elite-based structure strategy is adopted to find a suitable number of fuzzy rules for solving a problem. The parameter learning is to adjust parameters of the NFIS. It is a hybrid evolutionary algorithm of cooperative particle swarm optimization (CPSO) and cultural algorithm, called cultural CPSO (CCPSO). The CCPSO, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Experimental results demonstrate that the proposed method performs well in predicting time series and solving nonlinear control problems. View full abstract»

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  • Fuzzy Turing Machines: Variants and Universality

    Publication Year: 2008 , Page(s): 1491 - 1502
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (210 KB) |  | HTML iconHTML  

    In this paper, we study some variants of fuzzy Turing machines (FTMs) and universal FTM. First, we give several formulations of FTMs, including, in particular, deterministic FTMs (DFTMs) and nondeterministic FTMs (NFTMs). We then show that DFTMs and NFTMs are not equivalent as far as the power of recognizing fuzzy languages is concerned. This contrasts sharply with classical TMs. Second, we show that there is no universal FTM that can exactly simulate any FTM on it. But if the membership degrees of fuzzy sets are restricted to a fixed finite subset A of [0,1], such a universal machine exists. We also show that a universal FTM exists in some approximate sense. This means, for any prescribed accuracy, that we can construct a universal machine that simulates any FTM with the given accuracy. Finally, we introduce the notions of fuzzy polynomial time-bounded computation and nondeterministic fuzzy polynomial time-bounded computation, and investigate their connections with polynomial time-bounded computation and nondeterministic polynomial time-bounded computation. View full abstract»

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  • Encoding Words Into Interval Type-2 Fuzzy Sets Using an Interval Approach

    Publication Year: 2008 , Page(s): 1503 - 1521
    Cited by:  Papers (50)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1117 KB) |  | HTML iconHTML  

    This paper presents a very practical type-2-fuzzistics methodology for obtaining interval type-2 fuzzy set (IT2 FS) models for words, one that is called an interval approach (IA). The basic idea of the IA is to collect interval endpoint data for a word from a group of subjects, map each subject's data interval into a prespecified type-1 (T1) person membership function, interpret the latter as an embedded T1 FS of an IT2 FS, and obtain a mathematical model for the footprint of uncertainty (FOU) for the word from these T1 FSs. The IA consists of two parts: the data part and the FS part. In the data part, the interval endpoint data are preprocessed, after which data statistics are computed for the surviving data intervals. In the FS part, the data are used to decide whether the word should be modeled as an interior, left-shoulder, or right-shoulder FOU. Then, the parameters of the respective embedded T1 MFs are determined using the data statistics and uncertainty measures for the T1 FS models. The derived T1 MFs are aggregated using union leading to an FOU for a word, and finally, a mathematical model is obtained for the FOU. In order that all researchers can either duplicate our results or use them in their research, the raw data used for our codebook examples, as well as a MATLAB M-file for the IA, have been put on the Internet at: http://sipi.usc.edu/ ~ mendel. View full abstract»

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  • A Fuzzy Qualitative Framework for Connecting Robot Qualitative and Quantitative Representations

    Publication Year: 2008 , Page(s): 1522 - 1530
    Cited by:  Papers (22)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (814 KB) |  | HTML iconHTML  

    This paper proposes a novel framework for describing articulated robot kinematics motion with the goal of providing a unified representation by combining symbolic or qualitative functions and numerical sensing and control tasks in the context of intelligent robotics. First, fuzzy qualitative robot kinematics that provides theoretical preliminaries for the proposed robot motion representation is revisited. Second, a fuzzy qualitative framework based on clustering techniques is presented to connect numerical and symbolic robot representations. Built on the k-bb AGOP operator (an extension of the ordered weighted aggregation operators), k-means and Gaussian functions are adapted to model a multimodal density of fuzzy qualitative kinematics parameters of a robot in both Cartesian and joint spaces; on the other hand, a mixture regressor and interpolation method are employed to convert Gaussian symbols into numerical values. Finally, simulation results in a PUMA 560 robot demonstrated that the proposed method effectively provides a two-way connection for robot representations used for both numerical and symbolic robotic tasks. View full abstract»

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  • Domain Representation Using Possibility Theory: An Exploratory Study

    Publication Year: 2008 , Page(s): 1531 - 1541
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (216 KB) |  | HTML iconHTML  

    This study explores a new domain representation method for natural language processing based on an application of possibility theory. In our method, domain-specific information is extracted from natural language documents using a mathematical process based on Rieger's notion of semantic distances, and represented in the form of possibility distributions. We implement the distributions in the context of a possibilistic domain classifier, which is trained using the SchoolNet corpus. View full abstract»

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  • An Efficient Pruning Method for Decision Alternatives of OWA Operators

    Publication Year: 2008 , Page(s): 1542 - 1549
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (401 KB) |  | HTML iconHTML  

    In this paper, we present an efficient method for pruning decision alternatives in the case of using ordered weighted averaging (OWA) operators for decision making. The proposed method helps to identify inferior alternatives that are less likely to be selected out of competing alternatives as the OWA aggregation proceeds. It thus enables us to diminish the number of alternatives before applying the OWA operators. The reordering process unique to the OWA aggregation plays an important role in identifying inferior alternatives. The efficacy of the proposed method is demonstrated by simulation analysis in which artificial decision problems of diverse sizes are generated and then examined with four scenarios: pruning alternatives, pruning alternatives with rank-order OWA weights, pruning alternatives with a normalized decision problem, and pruning alternatives with both a normalized decision problem and rank-order OWA weights. The proposed method is easy to use, and the simulation results show that the number of alternatives can be reduced drastically by applying this method. View full abstract»

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  • Perceptual Reasoning for Perceptual Computing

    Publication Year: 2008 , Page(s): 1550 - 1564
    Cited by:  Papers (24)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (683 KB) |  | HTML iconHTML  

    In 1996, Zadeh proposed the paradigm of computing with words (CWW). A specific architecture for making subjective judgments using CWW was proposed by Mendel in 2001. It is called a Perceptual Computer (Per-C), and because words can mean different things to different people, it uses interval type-2 fuzzy set (IT2 FS) models for all words. The Per-C has three elements: the encoder, which transforms linguistic perceptions into IT2 FSs that activate a CWW engine; the decoder, which maps the output of a CWW engine back into a word; and the CWW engine. Although di-fferent kinds of CWW engines are possible, this paper only focuses on CWW engines that are rule-based and the computations that map its input IT2 FSs into its output IT2 FS. Five assumptions are made for a rule-based CWW engine, the most important of which is: The result of combining fired rules must lead to a footprint of uncertainty (FOU) that resembles the three kinds of FOU that have previously been shown to model words (interior, left-shoulder, and right-shoulder FOUs). Requiring this means that the output FOU from a rule-based CWW engine will look similar in shape to an FOU in a codebook (i.e., a vocabulary of words and their respective FOUs) for an application, so that the decoder can therefore sensibly establish the word most similar to the CWW engine output FOU. Because existing approximate reasoning methods do not satisfy this assumption, a new kind of rule-based CWW engine is proposed, one that is called Perceptual Reasoning, and is proved to always satisfy this assumption. Additionally, because all IT2 FSs in the rules as well as those that excite the rules are either an interior, left-shoulder, or right-shoulder FOU, it is possible to carry out the sup-min calculations that are required by the inference engine, and those calculations are also in this paper. The results in this paper let us implement a rule-based CWW engine for the Per-C. View full abstract»

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  • Fuzzy Clustering and Aggregation of Relational Data With Instance-Level Constraints

    Publication Year: 2008 , Page(s): 1565 - 1581
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1456 KB) |  | HTML iconHTML  

    In this paper, we introduce a semisupervised approach for clustering and aggregating relational data (SS-CARD). We assume that data is available in a relational form, where information only about the degrees to which pairs of objects in the dataset are related is available. Moreover, we assume that the relational information is represented by multiple dissimilarity matrices. These matrices could have been generated using different features, different mappings, or even different sensors. SS-CARD is designed to aggregate pairwise distances from multiple relational matrices, partition the data into clusters, and learn a relevance weight for each matrix in each cluster simultaneously. These weights have two main advantages. First, they help in partitioning the data into more meaningful clusters. Second, they can be used as part of a more complex learning system to enhance its learning behavior. SS-CARD uses partial supervision information that consists of a small set of constraints on which instances (should link) or ( should not link) reside in the same cluster. This additional information can guide the algorithm in learning the optimal relevance weights and in generating a better partition. The performance of the proposed algorithm is illustrated by using it in two different applications. The first one consists of categorizing the discrete nominal-valued mushroom data. The second application consists of categorizing a collection of images where each image is represented by several continuous features. For both applications, we represent the pairwise image dissimilarities by multiple relational matrices extracted from different feature sets. The results are compared with those obtained by three traditional relational clustering methods. We show that the partial supervision information and the learned aggregation weights can improve the results significantly. View full abstract»

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  • Performance of the IDS Method as a Soft Computing Tool

    Publication Year: 2008 , Page(s): 1582 - 1596
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (587 KB) |  | HTML iconHTML  

    Performance factors such as robustness, speed, and tractability are important for the realization of practical computing systems. The aim of soft computing is to achieve these factors in practice by tolerating imprecision and uncertainty instead of depending on exact mathematical computations. The ink drop spread (IDS) method is a modeling technique that has been proposed as a new approach to soft computing. This method is characterized by a modeling process that uses image information without including complex formulas. In this study, the performance of the IDS method is investigated in terms of robustness, speed, and tractability, which are typical criteria that determine the importance of soft computing tools. Robustness is evaluated on the basis of noise tolerance and fault tolerance. Tractability is discussed from the viewpoints of interpretability and transparency. Based on comparative evaluations with artificial neural networks and fuzzy inference systems, this study demonstrates that the IDS method has superior capability to function as a soft computing tool. View full abstract»

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  • A Hammerstein Recurrent Neurofuzzy Network With an Online Minimal Realization Learning Algorithm

    Publication Year: 2008 , Page(s): 1597 - 1612
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (375 KB) |  | HTML iconHTML  

    This paper presents a Hammerstein recurrent neurofuzzy network associated with an online minimal realization learning algorithm for dealing with nonlinear dynamic applications. We fuse the concept of states in linear systems into a neurofuzzy framework so that the whole structure can be expressed by a state-space representation. An online minimal realization learning algorithm has been developed to find a controllable and observable state-space model of minimal size from the input-output measurements of a given system. Such an idea can simultaneously resolve the problem of the determination of a minimal structure and the difficulty of network stability analysis. The advantages of our approach include: 1) our recurrent network is capable of translating the complicated dynamic behavior of a nonlinear system into a minimal set of linguistic fuzzy dynamical rules and into state-space representation as well and 2) an online minimal realization learning algorithm unifies an order determination algorithm, a hybrid parameter initialization method, and a recursive recurrent learning algorithm into a systematic procedure to identify a minimal structure with satisfactory performance. Performance evaluations on benchmark examples as well as real-world applications have successfully validated the effectiveness of our approach. View full abstract»

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  • Analytical Study and Stability Design of a 3-D Fuzzy Logic Controller for Spatially Distributed Dynamic Systems

    Publication Year: 2008 , Page(s): 1613 - 1625
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (552 KB) |  | HTML iconHTML  

    A novel 3-D fuzzy logic controller (3-D FLC) was presented to control a class of spatially distributed dynamic systems by Li (IEEE Trans. Fuzzy Syst., vol. 15, no. 3, pp. 470-481, Jun. 2007) by utilizing a 3-D fuzzy set and an inference mechanism with 3-D nature for spatial information processing. In this paper, the analytical mathematical model of the 3-D FLC is derived, and the controller structure is explained with the help of the existing conventional control techniques. The graphic analytical method for the traditional two-term FLC can be used for the analytical model derivation. The derived result shows that the 3-D FLC has a global sliding-mode structure over the spatial domain and explains why the 3-D FLC is able to process spatial information more effectively than its traditional counterpart using a few more sensors. Because of its sliding-mode feature, the Lyapunov stability criterion can be developed easily to analyze and design the 3-D FLC. Finally, a catalytic reactor is presented as an example to demonstrate the effectiveness of the 3-D FLC as compared with other controllers. View full abstract»

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  • Fuzzy Interpolative Reasoning for Sparse Fuzzy Rule-Based Systems Based on {bm \alpha } -Cuts and Transformations Techniques

    Publication Year: 2008 , Page(s): 1626 - 1648
    Cited by:  Papers (27)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (802 KB) |  | HTML iconHTML  

    In sparse fuzzy rule-based systems, the fuzzy rule bases are usually incomplete. In this situation, the system may not properly perform fuzzy reasoning to get reasonable consequences. In order to overcome the drawback of sparse fuzzy rule-based systems, there is an increasing demand to develop fuzzy interpolative reasoning techniques in sparse fuzzy rule-based systems. In this paper, we present a new fuzzy interpolative reasoning method via cutting and transformation techniques for sparse fuzzy rule-based systems. It can produce more reasonable results than the existing methods. The proposed method provides a useful way to deal with fuzzy interpolative reasoning in sparse fuzzy rule-based systems. View full abstract»

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  • Piecewise H_{\infty } Controller Design of Uncertain Discrete-Time Fuzzy Systems With Time Delays

    Publication Year: 2008 , Page(s): 1649 - 1655
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (145 KB) |  | HTML iconHTML  

    This paper considers the robust H infin control of uncertain discrete-time fuzzy systems with time delays based on piecewise Lyapunov--Krasovskii functionals. It is shown that the stability with H infin disturbance attenuation performance can be established for the closed-loop fuzzy control systems if there exists a piecewise Lyapunov--Krasovskii functional, and moreover, the functional and the corresponding controller can be obtained by solving a set of linear matrix inequalities that are numerically feasible. A numerical example is given to demonstrate the efficiency and the advantage of the proposed method. View full abstract»

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  • {cal H}_{\infty } State-Feedback Control Design for Fuzzy Systems Using Lyapunov Functions With Quadratic Dependence on Fuzzy Weighting Functions

    Publication Year: 2008 , Page(s): 1655 - 1663
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (183 KB) |  | HTML iconHTML  

    This paper proposes a method for designing an Hinfin state-feedback fuzzy controller for discrete-time Takagi-Sugeno (T-S) fuzzy systems. To derive less conservative Hinfin stabilization conditions, this paper enhances the interactions among the fuzzy subsystems using a multiple Lyapunov function with quadratic dependence on fuzzy weighting functions. Besides, for more allocation of the nonlinearity to the fuzzy control system, this paper introduces a slack variable that is quadratically dependent on the one-step-past fuzzy weighting functions as well as the current ones. In the derivation, the Hinfin stabilization conditions are formulated in terms of parameterized linear matrix inequalities (PLMIs), which are reconverted into LMI conditions with the help of an efficient relaxation technique. View full abstract»

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  • Corrections to “Aggregation Using the Linguistic Weighted Average and Interval Type-2 Fuzzy Sets”

    Publication Year: 2008 , Page(s): 1664 - 1666
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (185 KB)  

    In the previous paper, we have proposed linguistic weighted average (LWA) algorithms that can be used in distributed and hierarchical decision making. The original LWA algorithms were completely based on the representation theorem for interval type-2 fuzzy sets (IT2 FSs). In later usage, we found that when the lower membership functions (LMFs) of the inputs and weights are of different heights, the LMF of the output IT2 FS may be nonconvex and discontinuous. In this letter, a correction to the original LWA algorithms is proposed. The new LWA algorithms are simpler and easier to understand; so, it should facilitate the applications of the LWAs. View full abstract»

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  • 2008 Index IEEE Transactions on Fuzzy Systems Vol. 16

    Publication Year: 2008 , Page(s): 1667 - 1683
    Save to Project icon | Request Permissions | PDF file iconPDF (168 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