Showing papers on "Fuzzy logic published in 1993"

ANFIS: adaptive-network-based fuzzy inference system

Abstract: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >

Fuzzy Set Theory and Its Applications

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A fuzzy-logic-based approach to qualitative modeling

Abstract: This paper discusses a general approach to quali- tative modeling based on fuzzy logic. The method of qualitative modeling is divided into two parts: fuzzy modeling and linguistic approximation. It proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model. To clarify the advantages of the proposed method, it also shows some examples of modeling, among them a model of a dynamical process and a model of a human operator's control action.

An Introduction to Fuzzy Control

Abstract: Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic. They can be found either as stand-alone control elements or as int ...

Stable adaptive fuzzy control of nonlinear systems

Abstract: A direct adaptive fuzzy controller that does not require an accurate mathematical model of the system under control, is capable of incorporating fuzzy if-then control rules directly into the controllers, and guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded is developed. The specific formula for the bounds is provided, so that controller designers can determine the bounds based on their requirements. The direct adaptive fuzzy controller is used to regulate an unstable system to the origin and to control the Duffing chaotic system to track a trajectory. The simulation results show that the controller worked without using any fuzzy control rules, and that after fuzzy control rules were incorporated the adaptation speed became much faster. It is shown explicitly how the supervisory control forces the state to remain within the constraint set and how the adaptive fuzzy controller learns to regain control. >

Fuzzy Measure Theory

Abstract: Introduction. Required Background in Set Theory. Fuzzy Measures. Extensions. Structural Characteristics for Set Functions. Measurable Functions on Fuzzy Measure Spaces. Fuzzy Integrals. PanIntegrals. Applications. Index.

Functional equivalence between radial basis function networks and fuzzy inference systems

Abstract: It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent. This functional equivalence makes it possible to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is of interest to observe that two models stemming from different origins turn out to be functionally equivalent. >

Fuzzy measures and fuzzy integrals—a survey

Abstract: As is well-known in recent years, there are two kinds of uncertainities, randomness and fuzziness, which can be both dealt with from a mathematical point of view. We know the concept of probabilities with respect to randomness and also that of fuzzy sets with respect to fuzziness. This fact tempts us to discuss fuzzy sets in comparison with probabilities. However, such a direct comparison must fail. The concept of fuzzy sets (precisely speaking, fuzzy subsets of an ordinary set) is nothing but an extended concept of ordinary sets. We have to notice that the concept of probabilities is absolutely different from that of sets. To discuss our problem in detail, let us consider probabilities for the time being. There are a number of interpretations for probabilities: classical probabilities (originated by Laplace); measure theoretical probabilities (by Kolmogorov); subjective probabilities in Bayesian statistics; probabilities as logics and so on.

Fuzzy Thinking: The New Science of Fuzzy Logic

Abstract: Fuzzy logic is the next wave in technology. Japanese electronics giants have, in the last ten years, already staked their commmercial future on the benefits of fuzzy production; naturally, only this year have European and US companies begun to catch up. Fuzzy logic sanctifies vagueness. It prescribes a new way of thinking about machines, about science, ambiguity, confusion and contradiction.

Fuzzy gain scheduling of PID controllers

Abstract: This paper describes the development of a fuzzy gain scheduling scheme of PID controllers for process control. Fuzzy rules and reasoning are utilized online to determine the controller parameters based on the error signal and its first difference. Simulation results demonstrate that better control performance can be achieved in comparison with Ziegler-Nichols controllers and Kitamori's PID controllers. >

Fuzzy Sets and Interactive Multiobjective Optimization

Abstract: This monograph is the first to integrate ambiguous parameters in problem-formulation with fuzzy goals for multiobjective optimization into a unified methodology. Including ''real-world'' applications illustrated by interactive computer programs (written in C, version 6.0 for Ibm Pcs), the work is intended for advanced undergraduate and graduate students and specialists in systems analysis in such fields as public decision-making, administrative planning, and managerial decision-making.

Fuzzy control of pH using genetic algorithms

Abstract: Abstruct- Establishing suitable control of pH, a requirement in a number of mineral and chemical industries, poses a difficult problem because of inherent nonlinearities and frequently changing process dynamics. Researchers at the U.S. Bureau of Mines have developed a technique for producing adaptive fuzzy logic controllers (FLC’s) that are capable of effectively managing such systems. In this technique, a genetic algorithm (GA) alters the membership functions employed by a conventional FLC, an approach that is contrary to the tactic generally used to provide FLC’s with adaptive capabilities in which the rule set is altered. GA’s are search algorithms based on the mechanics of natural genetics that are able to rapidly locate near-optimal solutions to difficult problems. The Bureau-developed technique is used to produce an adaptive GA-FLC for a laboratory acid-base experiment. Nonlinearities in the laboratory system are associated with the logarithmic pH scale (pH is proportional to the logarithm of HJO’ ions) and changing process dynamics are introduced by altering system parameters such as the desired set point and the concentration and buffering capacity of input solutions. Results indicate that FLC’s augmented with GA’s offer a powerful alternative to conventional process control techniques in the nonlinear, rapidly changing pH systems commonly found in industry.

Switching regression models and fuzzy clustering

Abstract: A family of objective functions called fuzzy c-regression models, which can be used too fit switching regression models to certain types of mixed data, is presented. Minimization of particular objective functions in the family yields simultaneous estimates for the parameters of c regression models, together with a fuzzy c-partitioning of the data. A general optimization approach for the family of objective functions is given and corresponding theoretical convergence results are discussed. The approach is illustrated by two numerical examples that show how it can be used to fit mixed data to coupled linear and nonlinear models. >

Sensor and Data Fusion Concepts and Applications

Abstract: Multiple Sensor System Applications, Benefits, and Atmospheric Attenuation Data Fusion Algorithms and Architectures Bayesian Inference Dempster-Shafer Algorithm Artificial Neural Networks Voting Fusion Fuzzy Logic and Neural Networks Passive Data Association Techniques for Unambiguous Location of Targets. Appendices: Planck Radiation Law and Radiative Transfer Voting Fusion With Nested Confidence Levels.

Integrating design stage of fuzzy systems using genetic algorithms

Abstract: The authors propose an automatic fuzzy system design method that uses a genetic algorithm and integrates three design stages. The method determines membership functions, the number of fuzzy rules, and the rule-consequent parameters at the same time. Because these design stages may not be independent, it is important to consider them simultaneously to obtain optimal fuzzy systems. The method includes a genetic algorithm and a penalty strategy that favors systems with fewer rules. The method was applied to the classic inverted-pendulum control problem and has been shown to be practical through a comparison with another method. >

Fuzzy numbers: an overview

Abstract: This paper is an overview of past and present works dealing with fuzzy numbers and their operations A fuzzy number (more generally a fuzzy quantity) is a fuzzy set in the real line Particular cases include usual real numbers and intervals Usual operations on the real line canonically extend to operations between fuzzy quantities, thus extending the usual interval (or error) analysis to many-valued quantities What is obtained is a counterpart of random variable calculus, but where, contrary to the latter case, errors never compensate Many results pertaining to mathematical properties as well as calculation methods are now available and are summed up in the paper The dual theory — “optimistic” operations on fuzzy numbers, ie, with maximal error compensation — is also outlined; its interest lies in providing tools for solving fuzzy number equations Lastly, the problem of comparing fuzzy numbers is considered The paper includes some historical background, as well as an extensive bibliography of applications to mathematics and engineering

Intelligent systems for engineers and scientists

Abstract: The third edition of this bestseller examines the principles of artificial intelligence and their application to engineering and science, as well as techniques for developing intelligent systems to solve practical problems. Covering the full spectrum of intelligent systems techniques, it incorporates knowledge-based systems, computational intelligence, and their hybrids. Using clear and concise language, Intelligent Systems for Engineers and Scientists, Third Edition features updates and improvements throughout all chapters. It includes expanded and separated chapters on genetic algorithms and single-candidate optimization techniques, while the chapter on neural networks now covers spiking networks and a range of recurrent networks. The book also provides extended coverage of fuzzy logic, including type-2 and fuzzy control systems. Example programs using rules and uncertainty are presented in an industry-standard format, so that you can run them yourself. The first part of the book describes key techniques of artificial intelligence—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), frames, objects, agents, symbolic learning, case-based reasoning, genetic algorithms, optimization algorithms, neural networks, hybrids, and the Lisp and Prolog languages. The second part describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. The author provides sufficient detail to help you develop your own intelligent systems for real applications. Whether you are building intelligent systems or you simply want to know more about them, this book provides you with detailed and up-to-date guidance.

Neural networks that learn from fuzzy if-then rules

Abstract: An architecture for neural networks that can handle fuzzy input vectors is proposed, and learning algorithms that utilize fuzzy if-then rules as well as numerical data in neural network learning for classification problems and for fuzzy control problems are derived. The learning algorithms can be viewed as an extension of the backpropagation algorithm to the case of fuzzy input vectors and fuzzy target outputs. Using the proposed methods, linguistic knowledge from human experts represented by fuzzy if-then rules and numerical data from measuring instruments can be integrated into a single information processing system (classification system or fuzzy control system). It is shown that the scheme works well for simple examples. >

The paradoxical success of fuzzy logic

Abstract: This paper investigates the question of which aspects of fuzzy logic are essential to its practical usefulness. We show that as a formal system, a standard version of fuzzy logic collapses mathematically to two-valued logic, while empirically, fuzzy logic is not adequate for reasoning about uncertain evidence in expert systems. Nevertheless, applications of fuzzy logic in heuristic control have been highly successful. We argue that the inconsistencies of fuzzy logic have not been harmful in practice because current fuzzy controllers are far simpler than other knowledge-based systems. In the future, the technical limitations of fuzzy logic can be expected to become important in practice, and work on fuzzy controllers will also encounter several problems of scale already known for other knowledge-based systems.

Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques

Abstract: This paper proposes using fuzzy logic techniques to dynamically control parameter settings of genetic algorithms (GAs). We describe the Dynamic Parametric GA: a GA that uses a fuzzy knowledge-based system to control GA parameters. We then introduce a technique for automatically designing and tuning the fuzzy knowledge-base system using GAs. Results from initial experiments show a performance improvement over a simple static GA. One Dynamic Parametric GA system designed by our automatic method demonstrated improvement on an application not included in the design phase, which may indicate the general applicability of the Dynamic Parametric GA to a wide range of applications.

Virtual worlds as fuzzy cognitive maps

Abstract: Fuzzy cognitive maps (FCMs) can structure virtual worlds. FCMs link causal events, values, goals, and trends in a fuzzy feedback dynamical system. They direct actors in virtual worlds as the actors react to events and to one another. In nested FCMs each causal concept can control its own FCM. This combines levels of fuzzy systems that can choose goals or move objects. Adaptive FCMs change as causal patterns change. They adapt with differential Hebbian learning. FCMs are applied to an undersea virtual world of dolphins. >

A fuzzy inference engine in nonlinear analog mode and its application to a fuzzy logic control

Abstract: In this tutorial, the utility of a fuzzy system is demonstrated by providing a broad overview, emphasizing analog mode hardware, along with a discussion of the author's original work. First, the difference between deterministic words and fuzzy words is explained as well as fuzzy logic. The description of the system using mathematical equations, linguistic rules, or parameter distributions (e.g., neural networks) is discussed. Fuzzy inference and defuzzification algorithms are presented, and their hardware implementation is discussed. The fuzzy logic controller was used to stabilize a glass with wine balanced on a finger and a mouse moving around a plate on the tip of an inverted pendulum. >

Fuzzy Control Systems

Abstract: Foreword (L.A. Zadeh). Preface (A. Kandel and G. Langholz). General Theory: Learning Algorithms for Neuro-Fuzzy Networks (P.Y. Glorennec). Towards a Unified Theory of Intelligent Autonomous Control Systems (L.J. Kohout). Reasoning by Analogy in Fuzzy Controllers (W. Pedrycz). Information Complexity and Fuzzy Control (A. Ramer and V. Kreinovich). Alternative Structures for Knowledge Representation in Fuzzy Logic Controllers (R.R. Yager). Methodologies and Algorithms: Dynamic Analysis of Fuzzy Logic Control Structures (A. Garcia-Cerezo, A. Ollero, and J. Aracil). Intelligent Fuzzy Controller for Event-Driven, Real Time Systems and its VLSI Implementation (J. Grantner, M. Patyra, and M.S. Stachowicz). Constraint-Oriented Fuzzy Control Schemes for Cart-Pole Systems by Goal Decoupling and Genetic Algorithms (O. Katai, M. Ida, T. Sawaragi, S. Iwai, S. Kohno, and T. Kataoka). A Self Generating and Tuning Method for Fuzzy Modeling Using Interior Penalty Method and Its Application to Knowledge Acquisition of Fuzzy Controller (R. Katayama, Y. Kajitani, and Y. Nishida). Fuzzy Control of VSS Type and Its Robustness (S. Kawaji and N. Matsunaga). The Composition of Heterogeneous Control Laws (B. Kuipers and K. Astrom). Synthesis of Nonlinear Controllers Via Fuzzy Logic (R. Langari). Fuzzy Controls under Product-Sum-Gravity Methods and New Fuzzy Control Methods (M. Mizumoto). Fuzzy Modeling for Adaptive Process Control (Y. Nakamori). Fuzzy Controller with Matrix Representation (M. Nakatsuyama, J.H. Yan, and H. Kaminaga). A Self-Tuning Method of Fuzzy Reasoning by Genetic Algorithm (H. Nomura, I. Hayashi, and N. Wakami). Hybrid Neural-Fuzzy Reasoning Model with Application to Fuzzy Control (D. Park, A. Kandel, and G. Langholz). Learning Fuzzy Control Rules from Examples (S.G. Romaniuk and L.O. Hall). A Computational Approach to Fuzzy Logic Controller Design and Analysis Using Cell State Space Methods (S.M. Smith, B. Nokleby, and D.J. Comer). An Adaptive Fuzzy Control Model Based on Fuzzy Neural Networks (X. Zhang, P. Wang, Z. Shen, and X. Peng). Implementations and Applications: Human Friendly Fuzzy Transportation System (T. Iokibe and T. Kimura). Control of a Chaotic System Using Fuzzy Logic (C.L. Karr and E.J. Gentry). Applications of a Fuzzy Control Technique to Superconducting Actuators Using High-Tc Superconductors (M. Komori and T. Kitamura). A Fuzzy Logic Based Approach to Machine Tool Control Optimization (J.R.A. Lopez, E.A. Gutierrez, and L.C. Rosa). Fuzzy Management of Cache Memories (M.A. Manzoul). Fuzzy Controllers on Semi-Custom VLSI Chips (M.A. Manzoul). General Analysis of Fuzzy-Controlled Phase-Locked Loop (H.N. Teodorescu and A. Brezulianu). A Fuzzy Logic Controller for a Rigid Disk Drive (S. Yoshida). Author's Biographical Information. Index.