By Topic

Fuzzy Systems, IEEE Transactions on

Issue 4 • Date Aug. 2001

Filter Results

Displaying Results 1 - 19 of 19
  • Guest editorial fuzzy logic at the turn of the millennium

    Page(s): 481 - 482
    Save to Project icon | Request Permissions | PDF file iconPDF (21 KB)  
    Freely Available from IEEE
  • Comments on "Reduction of fuzzy rule base via singular value decomposition"

    Page(s): 675 - 677
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (38 KB) |  | HTML iconHTML  

    Several comments are presented for the reduction of the fuzzy rule base in the paper of Yam et al. (1999). In their paper, the approach to determine the number of singular values necessary for the reduction process to obtain the effective and most efficient fuzzy rule base is not provided. Although the output error of the fuzzy controller is bounded, the performance of the system output may not be satisfied. Moreover, the computation load is increased for each input of the fuzzy mechanism since the input membership functions are modified. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Author's reply

    Page(s): 676 - 677
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (23 KB)  

    First Page of the Article
    View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks

    Page(s): 578 - 594
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (380 KB)  

    A fast approach for automatically generating fuzzy rules from sample patterns using generalized dynamic fuzzy neural networks (GD-FNNs) is presented. The GD-FNN is built based on ellipsoidal basis functions and functionally is equivalent to a Takagi-Sugeno-Kang fuzzy system. The salient characteristics of the GD-FNN are: (1) structure identification and parameters estimation are performed automatically and simultaneously without partitioning input space and selecting initial parameters a priori; (2) fuzzy rules can be recruited or deleted dynamically; (3) fuzzy rules can be generated quickly without resorting to the backpropagation (BP) iteration learning, a common approach adopted by many existing methods. The GD-FNN is employed in a wide range of applications ranging from static function approximation and nonlinear system identification to time-varying drug delivery system and multilink robot control. Simulation results demonstrate that a compact and high-performance fuzzy rule-base can be constructed. Comprehensive comparisons with other latest approaches show that the proposed approach is superior in terms of learning efficiency and performance View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Low-complexity fuzzy relational clustering algorithms for Web mining

    Page(s): 595 - 607
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (304 KB)  

    This paper presents new algorithms-fuzzy c-medoids (FCMdd) and robust fuzzy c-medoids (RFCMdd)-for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total fuzzy dissimilarity within each cluster is minimized. A comparison of FCMdd with the well-known relational fuzzy c-means algorithm (RFCM) shows that FCMdd is more efficient. We present several applications of these algorithms to Web mining, including Web document clustering, snippet clustering, and Web access log analysis View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A neuro-fuzzy supervisory control system for industrial batch processes

    Page(s): 570 - 577
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (184 KB)  

    The automation of complex industrial batch processes is a difficult problem due to the extremely nonlinear and variable system behavior or the conflicting goals within the different process phases. The introduction of a single multiple-input multiple-output controller is not useful because of the rather high design effort and the low transparency of its complex structure. A more suitable hierarchical fuzzy-logic (FL) based supervisory control concept is proposed. It permits the decomposition of the complex control problem into a series of smaller and simpler ones. In the upper level of the hierarchy the FL-based supervisory controller classifies the actual process phase in terms of the available process sensor signals and activates dynamically the appropriate situation specific low-level controllers. The paper presents the generic concept of the FL supervisory controller that comprises both a FL process diagnosis and a control mode selection as well as experiences with the industrial application View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Nonlinear state feedback controller for nonlinear systems: Stability analysis and design based on fuzzy plant model

    Page(s): 657 - 661
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (144 KB) |  | HTML iconHTML  

    This paper presents the stability analysis of a fuzzy-model-based control system consisting of a nonlinear plant and a nonlinear state feedback controller and the design of the nonlinear gains of the controller. The nonlinear plant is represented by a fuzzy model having p rules. A nonlinear state feedback controller is designed to close the feedback loop. Under this design, the stability condition is reduced to p linear matrix inequalities. An application example on stabilizing a mass-spring-damper system will be given View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Affect analysis of text using fuzzy semantic typing

    Page(s): 483 - 496
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (388 KB) |  | HTML iconHTML  

    We propose a novel, convenient fusion of natural language processing and fuzzy logic techniques for analyzing the affect content in free text. Our main goals are fast analysis and visualization of affect content for decision making. The main linguistic resource for fuzzy semantic typing is the fuzzy-affect lexicon, from which other important resources, the fuzzy thesaurus and affect category groups, are generated. Free text is tagged with affect categories from the lexicon and the affect categories' centralities and intensities are combined using techniques from fuzzy logic to produce affect sets: fuzzy sets representing the affect quality of a document. We show different aspects of affect analysis using news content and movie reviews. Our experiments show a good correspondence between affect sets and human judgments of affect content. We ascribe this to the representation of ambiguity in our fuzzy affect lexicon and the ability of fuzzy logic to deal successfully with the ambiguity of words in a natural language View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Fuzzy approaches to the game of Chicken

    Page(s): 608 - 623
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (352 KB) |  | HTML iconHTML  

    Game theory deals with decision-making processes involving two or more parties, also known as players, with partly or completely conflicting interests. Decision-makers in a conflict must often make their decisions under risk and under unclear or fuzzy information. In this paper, two distinct fuzzy approaches are employed to investigate an extensively studied 2×2 game model-the game of Chicken. The first approach uses a fuzzy multicriteria decision analysis method to obtain optimal strategies for the players. It incorporates subjective factors into the decision-makers' objectives and aggregates objectives using a weight vector. The second approach applies the theory of fuzzy moves (TFM) to the game of Chicken. The theory of moves (TOM) is designed to bring a dynamic dimension to the classical theory of games by allowing decision-makers to look ahead for one or several steps so that they can make a better decision. TOM is the crisp counterpart of TFM, the approach we implement here to deal with games that include fuzzy and uncertain information. The application of fuzzy approaches to the game of Chicken demonstrates their effectiveness in manipulating subjective, uncertain, and fuzzy information and provides valuable insights into the strategic aspects of Chicken View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Compact and transparent fuzzy models and classifiers through iterative complexity reduction

    Page(s): 516 - 524
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (232 KB) |  | HTML iconHTML  

    In our previous work (2000) we showed that genetic algorithms (GAs) provide a powerful tool to increase the accuracy of fuzzy models for both systems modeling and classification. In addition to these results, we explore the GA to find redundancy in the fuzzy model for the purpose of model reduction. An aggregated similarity measure is applied to search for redundancy in the rule base description. As a result, we propose an iterative fuzzy identification technique starting with data-based fuzzy clustering with an overestimated number of local models. The GA is then applied to find redundancy among the local models with a criterion based on maximal accuracy and maximal set similarity. After the reduction steps, the GA is applied with another criterion searching for minimal set similarity and maximal accuracy. This results in an automatic identification scheme with fuzzy clustering, rule base simplification and constrained genetic optimization with low-human intervention. The proposed modeling approach is then demonstrated for a system identification and a classification problem View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The shape of fuzzy sets in adaptive function approximation

    Page(s): 637 - 656
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (876 KB) |  | HTML iconHTML  

    The shape of if-part fuzzy sets affects how well feedforward fuzzy systems approximate continuous functions. We explore a wide range of candidate if-part sets and derive supervised learning laws that tune them. Then we test how well the resulting adaptive fuzzy systems approximate a battery of test functions. No one shape emerges as the best. The sine function often does well and has tractable learning, but its undulating side-lobes may have no linguistic meaning. This suggests that function-approximation accuracy may sometimes have to outweigh linguistic or philosophical interpretations. We divide the if-part sets into two large classes. The first consists of n-dimensional joint sets that factor into n scalar sets. These sets ignore the correlations among input vector components. Fuzzy systems suffer in general from exponential rule explosion in high dimensions when they blindly approximate functions. The factorable fuzzy sets themselves also suffer from a curse of dimensionality: they tend to become binary spikes in high dimension. The second class consists of the more general but less common n-dimensional joint sets that do not factor into n scalar fuzzy sets. We present a method for constructing such unfactorable joint sets from scalar distance measures. Fuzzy systems that use unfactorable sets need not suffer from exponential rule explosion but their increased complexity may lead to intractable learning and inscrutable if-then rules. We prove that some of these sets still suffer from spikiness View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A trial for data retrieval using conceptual fuzzy sets

    Page(s): 497 - 505
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (232 KB) |  | HTML iconHTML  

    We describe trial applications of fuzzy sets to data retrieval. The objectives are to test their ability to achieve conceptual matching between retrieved objects and the user's intention and to connect real data with symbolic notations. The algorithm proposed retrieves data that conceptually fit the meanings of the entered keyword. An algorithm is described that uses fuzzy sets to handle word ambiguity (the main cause of vagueness in the meaning of a word). It is based on conceptual fuzzy sets (CFSs), which represent the meaning of words by chaining other related words. Two trial applications of this algorithm to data retrieval are described. First, an application to image retrieval shows variation of data retrieval with conceptual matching and transformation of numeric values into symbols. Next, an application to the agent recommending a TV program shows the method that lets CFSs fit to the sense of a user by Hebbian learning View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Criteria importances in median-like aggregation

    Page(s): 662 - 666
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (148 KB) |  | HTML iconHTML  

    An axiomatization of criteria importances appearing in a given aggregation operator is proposed. Some distinguished examples are recalled. For the class of k-medians, an integral based approach for inclusion of criteria importances is introduced. Several examples are given. The case of ordinal scales is also discussed View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Model construction, rule reduction, and robust compensation for generalized form of Takagi-Sugeno fuzzy systems

    Page(s): 525 - 538
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (388 KB) |  | HTML iconHTML  

    This paper presents a systematic procedure of fuzzy control system design that consists of fuzzy model construction, rule reduction, and robust compensation for nonlinear systems. The model construction part replaces the nonlinear dynamics of a system with a generalized form of Takagi-Sugeno fuzzy systems, which is newly developed by us. The generalized form has a decomposed structure for each element of Ai and Bi matrices in consequent parts. The key feature of this structure is that it is suitable for constructing IF-THEN rules and reducing the number of IF-THEN rules. The rule reduction part provides a successive procedure to reduce the number of IF-THEN rules. Furthermore, we convert the reduction error between reduced fuzzy models and a system to model uncertainties of reduced fuzzy models. The robust compensation part achieves the decay rate controller design guaranteeing robust stability for the model uncertainties. Finally, two examples demonstrate the utility of the systematic procedure developed View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Stable model reference adaptive fuzzy control of a class of nonlinear systems

    Page(s): 624 - 636
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (316 KB) |  | HTML iconHTML  

    In this paper, we propose a new adaptive fuzzy control scheme called model reference adaptive fuzzy control (MRAFC). The MRAFC scheme employs a reference model to provide closed-loop performance feedback for generating or modifying a fuzzy controller's knowledge base. The MRAFC scheme grew from ideas in conventional model reference adaptive control (MRAC). The MRAFC scheme is developed to perform adaptive feedback linearization to a class of nonlinear systems. A class of fuzzy controllers, which can be expressed in an explicit form, is used as the primary controller. Based on Lyapunov's second method, we have developed MRAFC schemes and derived fuzzy rule adaptive laws. Hence, not only the stability of the system can be assured but also the performance, such as the issues of robustness and parameter convergence, of the MRAFC system can be analyzed explicitly. We showed that in the case of no modeling error, the state error converges to zero asymptotically. In the case that persistent excitation is satisfied, we showed that the MRAFC system is asymptotically stable. By considering the periodic signal as reference input signal, we showed that the square wave can make the MRAFC system be persistently excited. The feasibility of applying these techniques has been demonstrated by considering the control of an inverted pendulum in following a reference model response View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A new sufficient condition for stable fuzzy control system and its design method

    Page(s): 554 - 569
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (320 KB) |  | HTML iconHTML  

    Fuzzy inference has a multigranular architecture consisting of symbols and continuous values, and this architecture has worked well to incorporate experts' know-how into fuzzy controls. The paper focuses on the important characteristic of fuzzy control, “symbolic expression” of a fuzzy control system. The paper first introduces a “chain of rules” to clarify the description of the behavior of the control systems. A coincidence of the symbolic and continuous behaviors is defined. With these definitions, the paper proposes a new condition, “relaxed nonseparate condition” and a new fuzzy inference method. The relaxed condition together with the new inference method guarantees the coincidence of symbolic and continuous behavior of the control system. The paper proposes a new design method of fuzzy controller that guarantees this coincidence. Simulations are done to show the feasibility of the relaxed nonseparate condition and proposed design method View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Effect of rule weights in fuzzy rule-based classification systems

    Page(s): 506 - 515
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (204 KB) |  | HTML iconHTML  

    This paper examines the effect of rule weights in fuzzy rule-based classification systems. Each fuzzy IF-THEN rule in our classification system has antecedent linguistic values and a single consequent class. We use a fuzzy reasoning method based on a single winner rule in the classification phase. The winner rule for a new pattern is the fuzzy IF-THEN rule that has the maximum compatibility grade with the new pattern. When we use fuzzy IF-THEN rules with certainty grades, the winner is determined as the rule with the maximum product of the compatibility grade and the certainty grade. In this paper, the effect of rule weights is illustrated by drawing classification boundaries using fuzzy IF-THEN rules with/without certainty grades. It is also shown that certainty grades play an important role when a fuzzy rule-based classification system is a mixture of general rules and specific rules. Through computer simulations, we show that comprehensible fuzzy rule-based systems with high classification performance can be designed without modifying the membership functions of antecedent linguistic values when we use fuzzy IF-THEN rules with certainty grades View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base

    Page(s): 667 - 674
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (156 KB) |  | HTML iconHTML  

    A method is proposed to automatically learn the knowledge base by finding an appropiate data base by means of a genetic algorithm while using a simple generation method to derive the rule base. Our genetic process learns the number of linguistic terms per variable and the membership function parameters that define their semantics, while a rule base generation method learns the number of rules and their composition View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • LMI-based fuzzy chaotic synchronization and communications

    Page(s): 539 - 553
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (360 KB) |  | HTML iconHTML  

    Addresses synthesis approaches for signal synchronization and secure communications of chaotic systems by using fuzzy system design methods based on linear matrix inequalities (LMIs). By introducing a fuzzy modeling methodology, many well-known continuous and discrete chaotic systems can be exactly represented by Takagi-Sugeno (T-S) fuzzy models with only one premise variable. Following the general form of fuzzy chaotic models, the structure of the response system is first proposed. Then, according to the applications of synchronization to the fuzzy models that have common bias terms or the same premise variable of drive and response systems, the driving signals are developed with four different types: fuzzy, character, crisp, and predictive driving signals. Synthesizing from the observer and controller points of view, all types of drive-response systems achieve asymptotic synchronization. For chaotic communications, the asymptotical recovering of messages is ensured by the same framework. It is found that many well-known chaotic systems can achieve their applications on asymptotical synchronization and recovering messages in secure communication by using either one type of driving signals or all. Several numerical simulations are shown with expected satisfactory performance View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

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