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Showing papers on "Fuzzy logic published in 1995"


Book
01 Jan 1995
TL;DR: Fuzzy Sets and Fuzzy Logic is a true magnum opus; it addresses practically every significant topic in the broad expanse of the union of fuzzy set theory and fuzzy logic.
Abstract: Fuzzy Sets and Fuzzy Logic is a true magnum opus. An enlargement of Fuzzy Sets, Uncertainty, and Information—an earlier work of Professor Klir and Tina Folger—Fuzzy Sets and Fuzzy Logic addresses practically every significant topic in the broad expanse of the union of fuzzy set theory and fuzzy logic. To me Fuzzy Sets and Fuzzy Logic is a remarkable achievement; it covers its vast territory with impeccable authority, deep insight and a meticulous attention to detail. To view Fuzzy Sets and Fuzzy Logic in a proper perspective, it is necessary to clarify a point of semantics which relates to the meanings of fuzzy sets and fuzzy logic. A frequent source of misunderstanding fias to do with the interpretation of fuzzy logic. The problem is that the term fuzzy logic has two different meanings. More specifically, in a narrow sense, fuzzy logic, FLn, is a logical system which may be viewed as an extension and generalization of classical multivalued logics. But in a wider sense, fuzzy logic, FL^ is almost synonymous with the theory of fuzzy sets. In this context, what is important to recognize is that: (a) FLW is much broader than FLn and subsumes FLn as one of its branches; (b) the agenda of FLn is very different from the agendas of classical multivalued logics; and (c) at this juncture, the term fuzzy logic is usually used in its wide rather than narrow sense, effectively equating fuzzy logic with FLW In Fuzzy Sets and Fuzzy Logic, fuzzy logic is interpreted in a sense that is close to FLW. However, to avoid misunderstanding, the title refers to both fuzzy sets and fuzzy logic. Underlying the organization of Fuzzy Sets and Fuzzy Logic is a fundamental fact, namely, that any field X and any theory Y can be fuzzified by replacing the concept of a crisp set in X and Y by that of a fuzzy set. In application to basic fields such as arithmetic, topology, graph theory, probability theory and logic, fuzzification leads to fuzzy arithmetic, fuzzy topology, fuzzy graph theory, fuzzy probability theory and FLn. Similarly, hi application to applied fields such as neural network theory, stability theory, pattern recognition and mathematical programming, fuzzification leads to fuzzy neural network theory, fuzzy stability theory, fuzzy pattern recognition and fuzzy mathematical programming. What is gained through fuzzification is greater generality, higher expressive power, an enhanced ability to model real-world problems and, most importantly, a methodology for exploiting the tolerance for imprecision—a methodology which serves to achieve tractability,

7,131 citations


Journal ArticleDOI
01 Mar 1995
TL;DR: The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models, which possess certain advantages over neural networks.
Abstract: Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called adaptive-network-based fuzzy inference system (ANFIS), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed. >

2,260 citations


Journal ArticleDOI
TL;DR: What are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system-identification application of these techniques are described, from a user's perspective.

2,031 citations


Journal ArticleDOI
01 Mar 1995
TL;DR: After synthesizing a FLS, it is demonstrated that it can be expressed mathematically as a linear combination of fuzzy basis functions, and is a nonlinear universal function approximator, a property that it shares with feedforward neural networks.
Abstract: A fuzzy logic system (FLS) is unique in that it is able to simultaneously handle numerical data and linguistic knowledge. It is a nonlinear mapping of an input data (feature) vector into a scalar output, i.e., it maps numbers into numbers. Fuzzy set theory and fuzzy logic establish the specifics of the nonlinear mapping. This tutorial paper provides a guided tour through those aspects of fuzzy sets and fuzzy logic that are necessary to synthesize an FLS. It does this by starting with crisp set theory and dual logic and demonstrating how both can be extended to their fuzzy counterparts. Because engineering systems are, for the most part, causal, we impose causality as a constraint on the development of the FLS. After synthesizing a FLS, we demonstrate that it can be expressed mathematically as a linear combination of fuzzy basis functions, and is a nonlinear universal function approximator, a property that it shares with feedforward neural networks. The fuzzy basis function expansion is very powerful because its basis functions can be derived from either numerical data or linguistic knowledge, both of which can be cast into the forms of IF-THEN rules. >

2,024 citations


Journal ArticleDOI
TL;DR: A fuzzy decision tree induction method, which is based on the reduction of classification ambiguity with fuzzy evidence, is developed, which represents classification knowledge more naturally to the way of human thinking and are more robust in tolerating imprecise, conflict, and missing information.

902 citations


Journal ArticleDOI
TL;DR: A synthesis on the use of fuzzy integral as an aggregation operator in multicriteria decision making and compared to those of usual aggregation operators is presented.

680 citations


Journal ArticleDOI
TL;DR: This paper examines the applicability of genetic algorithms in the simultaneous design of membership functions and rule sets for fuzzy logic controllers and examines the design of a robust controller for the cart problem and its ability to overcome faulty rules.
Abstract: This paper examines the applicability of genetic algorithms (GA's) in the simultaneous design of membership functions and rule sets for fuzzy logic controllers. Previous work using genetic algorithms has focused on the development of rule sets or high performance membership functions; however, the interdependence between these two components suggests a simultaneous design procedure would be a more appropriate methodology. When GA's have been used to develop both, it has been done serially, e.g., design the membership functions and then use them in the design of the rule set. This, however, means that the membership functions were optimized for the initial rule set and not the rule set designed subsequently. GA's are fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. This new method has been applied to two problems, a cart controller and a truck controller. Beyond the development of these controllers, we also examine the design of a robust controller for the cart problem and its ability to overcome faulty rules. >

673 citations


Journal ArticleDOI
01 Apr 1995
TL;DR: A fundamental theoretical question on why fuzzy control has such a good performance for a wide variety of practical problems is considered, and it is proved that for each fixed fuzzy logic belonging to a wide class of fuzzy logics, the fuzzy logic control systems using these two and any method of defuzzification are capable of approximating any real continuous function on a compact set to arbitrary accuracy.
Abstract: In this paper, we consider a fundamental theoretical question on why does fuzzy control have such a good performance for a wide variety of practical problems. We try to answer this fundamental question by proving that for each fixed fuzzy logic belonging to a wide class of fuzzy logics, and for each fixed type of membership function belonging to a wide class of membership functions, the fuzzy logic control systems using these two and any method of defuzzification are capable of approximating any real continuous function on a compact set to arbitrary accuracy. On the other hand, this result can be viewed as an existence theorem of an optimal fuzzy logic control system for a wide variety of problems. >

625 citations


Journal ArticleDOI
TL;DR: A new technique, based on fuzzy logic, for prioritizing failures for corrective actions in a Failure Mode, Effects and Criticality Analysis (FMECA), which allows the analyst to evaluate the risk associated with item failure modes directly using the linguistic terms that are employed in making the criticality assessment.

570 citations


Journal ArticleDOI
TL;DR: In this article, a sequential selection process in group decision making under linguistic assessments is presented, where a set of linguistic preference relations represents individuals preferences, and a collective linguistic preference is obtained by means of a defined linguistic ordered weighted averaging operator whose weights are chosen according to the concept of fuzzy majority, specified by a fuzzy linguistic quantifier.

563 citations


01 Jan 1995
TL;DR: A sequential selection process in group decision making under linguistic assessments is presented, where a set of linguistic preference relations represents individuals preferences and a collective linguistic preference is obtained by means of a defined linguistic ordered weighted averaging operator.
Abstract: IrL this paper, a sequential selection process in group decision making under linguistic assessments is presented, where a set of linguistic preference relations represents individuals preferences. A collective linguistic preference is obtained by means of a defined linguistic ordered weighted averaging operator whose weights are chosen according to the concept of fuzzy majority, specified by a fuzzy linguistic quantifier. Then we define the concepts of linguistic nondora nance, linguistic dominance, and strict dominance degrees as parts of the sequential selection process. The solution alternative(s) is obtained by applying these concepts.

Proceedings ArticleDOI
20 Mar 1995
TL;DR: A model-based fuzzy controller design utilizing the concept of so-called "parallel distributed compensation" is employed and the method is illustrated by application to the problem of balancing an inverted pendulum on a cart.
Abstract: We present a design methodology for stabilization of a class of nonlinear systems. First, we approximate a nonlinear plant with a Takagi-Sugeno fuzzy model. Then a model-based fuzzy controller design utilizing the concept of so-called "parallel distributed compensation" is employed. The design procedure is conceptually simple and straightforward. The method is illustrated by application to the problem of balancing an inverted pendulum on a cart. >

Journal ArticleDOI
18 Jun 1995
TL;DR: A variable speed wind generation system where fuzzy logic principles are used for efficiency optimization and performance enhancement control and the complete control system has been developed, analyzed, and validated by simulation study.
Abstract: The paper describes a variable speed wind generation system where fuzzy logic principles are used for efficiency optimization and performance enhancement control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which pumps power to a utility grid or can supply to an autonomous system. The generation system has fuzzy logic control with vector control in the inner loops. A fuzzy controller tracks the generator speed with the wind velocity to extract the maximum power. A second fuzzy controller programs the machine flux for light load efficiency improvement, and a third fuzzy controller gives robust speed control against wind gust and turbine oscillatory torque. The complete control system has been developed, analyzed, and validated by simulation study. Performances have then been evaluated in detail.

Journal ArticleDOI
TL;DR: A simple fuzzy-neural network is introduced for modeling systems, and it is proved that it can represent any continuous function over a compact set and provide a method to simplify the neural network.
Abstract: We develop simple but effective fuzzy-rule based models of complex systems from input-output data. We introduce a simple fuzzy-neural network for modeling systems, and we prove that it can represent any continuous function over a compact set. We introduce "fuzzy curves" and use them to: 1) identify significant input variables, 2) determine model structure, and 3) set the initial weights in the fuzzy-neural network model. Our method for input identification is computationally simple and, since we determine the proper network structure and initial weights in advance, we can train the network rapidly. Viewing the network as a fuzzy model gives insight into the real system, and it provides a method to simplify the neural network. >

Journal ArticleDOI
TL;DR: It is argued that many of the problems O&S discovered are due to difficulties that are intrinsic to fuzzy set theory, and that most of them disappear when fuzzy logic is replaced by supervaluation theory.

Book
30 Jun 1995
TL;DR: This book presents in a systematic and comprehensive manner themodeling of uncertainty, vagueness, or imprecision, alias "fuzziness," in just about any field of science and engineering as a usable methodology for modeling in the absence of real-time feedback.
Abstract: From the Publisher: This book presents in a systematic and comprehensive manner themodeling of uncertainty, vagueness, or imprecision, alias "fuzziness," in just about any field of science and engineering. It delivers a usable methodology for modeling in the absence of real-time feedback. The book includes a short introduction to fuzzy logic containing basic definitions of fuzzy set theory and fuzzy rule systems. It describes methods for the assessment of rule systems, systems with discrete response sets, for modeling time series, for exact physical systems, examines verification and redundancy issues, and investigates rule response functions. Definitions and propositions, some of which have not been published elsewhere, are provided; numerous examples as well as references to more elaborate case studies are also given. Fuzzy rule-based modeling has the potential to revolutionize fields such as hydrology because it can handle uncertainty in modeling problems too complex to be approached by a stochastic analysis. There is also excellent potential for handling large-scale systems such as regionalization or highly non-linear problems such as unsaturated groundwater pollution.


Journal ArticleDOI
TL;DR: The purpose of this paper is to present and discuss the different ways to build a fuzzy mathematical morphology, and compare their properties with respect to mathematical morphology and to fuzzy sets and interpret them in terms of logic and decision theory.

Journal ArticleDOI
Sung-Bae Cho1, Jong-Sung Kim1
01 Feb 1995
TL;DR: The authors propose a method for multinetwork combination based on the fuzzy integral that nonlinearly combines objective evidence, in the form of a fuzzy membership function, with subjective evaluation of the worth of the individual neural networks with respect to the decision.
Abstract: In the area of artificial neural networks, the concept of combining multiple networks has been proposed as a new direction for the development of highly reliable neural network systems. The authors propose a method for multinetwork combination based on the fuzzy integral. This technique nonlinearly combines objective evidence, in the form of a fuzzy membership function, with subjective evaluation of the worth of the individual neural networks with respect to the decision. The experimental results with the recognition problem of on-line handwriting characters confirm the superiority of the presented method to the other voting techniques. >

Journal ArticleDOI
TL;DR: A method is presented for tuning fuzzy control rules by genetic algorithms to make the fuzzy logic control systems behave as closely as possible to the operator or expert behavior in a control process.

Journal ArticleDOI
TL;DR: The author develops these events into complex frames and scripts, and describes in some detail the representation of an isa hierarchy using the statements in typicality logic.
Abstract: el->e2. The intended interpretation here is that whenever el occurs, so does e2 (or, el causes e2). However, the typicality operator can be used here to represent the fact that \"typically\" el causes e2, but not always. For example, if el=strike-thematch and e2=light-the-match, then typically, el causes e2, but not when additional information such as e3=match-is-wet is added. Thus, the preference criteria developed to deal with declarative statements can be used to retract unplausible chains of causal effects given new information. The author than develops these events into complex frames and scripts, and describes in some detail the representation of an isa hierarchy using the statements in typicality logic. It should be noted here that this effort does not differ substantially fi'om similar work on formalizing semantic networks except in the identification ofstatements in a given frame. For example, while every slot-value pair in a frame can be represented by a wff in predicate logic, a number of these formulas will be monotonic, while some will be typicality statements.

Proceedings ArticleDOI
08 Oct 1995
TL;DR: In this paper, a comparative evaluation of the proportional-integral, sliding mode and fuzzy logic controllers for applications to power converters is presented, and the mismatch between the characteristics which lead to varying performance is outlined.
Abstract: This paper presents a comparative evaluation of the proportional-integral, sliding mode and fuzzy logic controllers for applications to power converters. The mismatch between the characteristics which lead to varying performance is outlined. This paper also demonstrates certain similarities of both the fuzzy logic controller and sliding mode controller. Sensitivity of these controllers to supply voltage disturbances and load disturbances is studied and results are presented.



Book
01 Jan 1995
TL;DR: This book discusses Hybrid Systems with Case-Based Reasoning, Applications Using Hybrid Neural Networks with Fuzzy Logic and Genetic Algorithms, and the Future of Hybrid Intelligent Systems.
Abstract: Foreword L.A. Zadeh. Preface. 1. Overview of Intelligent Systems. 2. Research in Hybrid Intelligent Systems. 3. Expert Systems and Neural Networks.4. Industrial Experience: The Use of Hybrid Systems in the Power Industry. 5. Expert Networks. 6. Fuzzy Logic and Expert Systems. 7. Fuzzy Systems and Neural Networks. 8. Genetic Algorithms and Neural Networks. 9. Applications Using Hybrid Neural Networks with Fuzzy Logic and Genetic Algorithms. 10. Genetic Algorithms and Fuzzy Systems. 11. Adaptive Control of an Exothermic Chemical Reaction System Using Fuzzy Logic and Genetic Algorithms. 12. Genetic Algorithms and Expert Systems. 13. Hybrid Systems with Case-Based Reasoning. 14. Summary and the Future of Hybrid Intelligent Systems. References. Index.

Journal ArticleDOI
Shigeo Abe1, Ming-Shong Lan
TL;DR: A new method for extracting fuzzy rules directly from numerical input-output data for pattern classification by recursively resolving overlaps between two classes is discussed.
Abstract: In this paper, we discuss a new method for extracting fuzzy rules directly from numerical input-output data for pattern classification Fuzzy rules with variable fuzzy regions are defined by activation hyperboxes which show the existence region of data for a class and inhibition hyperboxes which inhibit the existence of data for that class These rules are extracted from numerical data by recursively resolving overlaps between two classes Then, optimal input variables for the rules are determined using the number of extracted rules as a criterion The method is compared with neural networks using the Fisher iris data and a license plate recognition system for various examples >

Book ChapterDOI
01 Jan 1995
TL;DR: This chapter is about an extension of additive measures, in particular probability measures, to a more general class of non-additive set functions.
Abstract: Chapter 5 was concerned with the theory of fuzzy sets as a mathematical model to describe vague concepts, and as an extension of ordinary set theory. In a similar spirit, this chapter is about an extension of additive measures, in particular probability measures, to a more general class of non-additive set functions.

Book
01 Dec 1995
TL;DR: This chapter discusses Neural-Network Fundamentals, which focuses on the foundations of the Fuzzy Network, and its application in Application Design and Financial Modeling.
Abstract: Foundations. Motivation. Neural-Network Fundamentals. Single Neuron Computations. Network Computations. Network Simulation. Foundations Summary. Suggested Readings. Bibliography. Paradigms. The Backpropagation Network. The Counterpropagation Network. Adaptive Resonance Theory. The Multidirectional Associative Memory. The Hopfield Memory. Network-Learning Summary. Suggested Readings. Bibliography. Application Design. Developing a Data Representation. Pattern Representation Methods. Exemplar Analysis. Training and Performance Evaluation. A Practical Example. Application-Design Summary. Suggested Readings. Bibliography. Associative Memories. Associative-Memory Definitions. Character Recognition. State-Space Search. Image Interpolation. Diagnostic Aids. Associative-Memory Summary. Suggested Readings. Bibliography. Business and Financial Applications. Financial Modeling. Market Prediction. Bond Rating. Predicting Commodity Futures. Financial-Applications Summary. Suggested Readings. Bibliography. Pattern Classification. NETtalk. Radar-Signature Classifier. Prostate-Cancer Detection. Pattern-Classification Summary. Suggested Readings. Bibliography. Image Processing. Image-Processing Networks. Gender Recognition from Facial Images. Imagery Feature Discovery. Aircraft Tracking in Video Imagery. Image-Processing Summary. Suggested Readings. Bibliography. Process Control and Robotics. Control Theory. Cart/Pole Balancer. Bipedal-Locomotion Control. Robotic Manipulator Control. Control-Application Summary. Suggested Readings. Bibliography. Fuzzy Neural Systems. Fuzzy Logic. Implementation of a Fuzzy Network. Fuzzy Neural Inference. Fuzzy Control of BPN Learning. Fuzzy Neural-System Summary. Suggested Readings. Bibliography. Answers to Selected Exercises. Index. 0201539217T04062001

Journal ArticleDOI
Hee Rak Beom1, Hyungsuck Cho1
01 Mar 1995
TL;DR: In this paper, a behavior selector using a bistable switching function chooses a behavior at each action step so that the mobile robot can go for the goal position without colliding with obstacles.
Abstract: The proposed navigator consists of an avoidance behavior and goal-seeking behavior. Two behaviors are independently designed at the design stage and then combined them by a behavior selector at the running stage. A behavior selector using a bistable switching function chooses a behavior at each action step so that the mobile robot can go for the goal position without colliding with obstacles. Fuzzy logic maps the input fuzzy sets representing the mobile robot's state space determined by sensor readings to the output fuzzy sets representing the mobile robot's action space. Fuzzy rule bases are built through the reinforcement learning which requires simple evaluation data rather than thousands of input-output training data. Since the fuzzy rules for each behavior are learned through a reinforcement learning method, the fuzzy rule bases can be easily constructed for more complex environments. In order to find the mobile robot's present state, ultrasonic sensors mounted at the mobile robot are used. The effectiveness of the proposed method is verified by a series of simulations. >

Journal ArticleDOI
01 Feb 1995
TL;DR: The notion of aggregation primitive is made primitive to the logic, and strength mappings from sets of arguments to one of a number of possible dictionaries are defined, which provides a uniform framework for reasoning under uncertainty.
Abstract: We present the syntax and proof theory of a logic of argumentation, LA. We also outline the development of a category theoretic semantics for LA. LA is the core of a proof theoretic model for reasoning under uncertainty. In this logic, propositions are labelled with a representation of the arguments which support their validity. Arguments may then be aggregated to collect more information about the potential validity of the propositions of interest. We make the notion of aggregation primitive to the logic, and then define strength mappings from sets of arguments to one of a number of possible dictionaries. This provides a uniform framework which incorporates a number of numerical and symbolic techniques for assigning subjective confidences to propositions on the basis of their supporting arguments. These aggregation techniques are also described, with examples