An Introduction to Fuzzy Logic Applications in Intelligent Systems

Overview

An Introduction to Fuzzy Logic Applications in Intelligent Systems consists of a collection of chapters written by leading experts in the field of fuzzy sets. Each chapter addresses an area where fuzzy sets have been applied to situations broadly related to intelligent systems.
The volume provides an introduction to and an overview of recent applications of fuzzy sets to various areas of intelligent systems. Its purpose is to provide information and easy access for people new ...
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Paperback (Softcover reprint of the original 1st ed. 1992)
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Overview

An Introduction to Fuzzy Logic Applications in Intelligent Systems consists of a collection of chapters written by leading experts in the field of fuzzy sets. Each chapter addresses an area where fuzzy sets have been applied to situations broadly related to intelligent systems.
The volume provides an introduction to and an overview of recent applications of fuzzy sets to various areas of intelligent systems. Its purpose is to provide information and easy access for people new to the field. The book also serves as an excellent reference for researchers in the field and those working in the specifics of systems development. People in computer science, especially those in artificial intelligence, knowledge-based systems, and intelligent systems will find this to be a valuable sourcebook. Engineers, particularly control engineers, will also have a strong interest in this book.
Finally, the book will be of interest to researchers working in decision support systems, operations research, decision theory, management science and applied mathematics. An Introduction to Fuzzy Logic Applications in Intelligent Systems may also be used as an introductory text and, as such, it is tutorial in nature.
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Product Details

Table of Contents

Knowledge Representation in Fuzzy Logic; L.A. Zadeh. Expert Systems Using Fuzzy Logic; R.R. Yager. Fuzzy Rules in Knowledge-Based Systems; D. Dubois, H. Prade. Fuzzy Logic Controllers; H. Berenji. Methods and Applications of Fuzzy Mathematical Programming; H.J. Zimmermann. Fuzzy Set Methods in Computer Vision; J.M. Keller, R. Krishnapuram. Fuzziness, Image Information and Scene Analysis; S.K. Pal. Fuzzy Sets in Natural Language Processing; V. Novak. Fuzzy-Set-Theoretic Applications in Modeling of Man-Machine Interactions; W. Karwowski, G. Salvendy. Questionnaires and Fuzziness; B. Bouchon-Meunier. Fuzzy Logic Knowledge Systems and Artificial Neural Networks in Medicine and Biology; E. Sanchez. The Representation and Use of Uncertainty and Metaknowledge in Milord; R. Lopez de Montaras, C. Sierra, J. Augusti. Fuzzy Logic with Linguistic Quantifiers in Group Decision Making; J. Kacprzyk, M. Fedrizzi, H. Nurmi. Learning in Uncertain Environments; M. Botta, A. Giordana, L. Saitta. Evidential Reasoning Under Probabilistic and Fuzzy Uncertainties; J.F. Baldwin. Probabilistic Sets-Probabilistic Extension of Fuzzy Sets; K. Hirota. Index.
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Sort by: Showing 1 Customer Reviews
  • Posted October 15, 2009

    more from this reviewer

    Good for theory, weak on implementing applications

    Invented by Lofti A. Zadeh in the middle sixties, the concepts of fuzzy logic and fuzzy sets remained primarily an academic curiosity for many years. It was only with the advent of "real world" computer reasoning that fuzziness began to enter the applications field. The phrase "real world" is used to refer to the much more common situations where characteristics are measured as likelihoods rather than the artificial strict containment of two-valued logic. With the recent explosions in the use of expert systems and neural networks, both largely dependent on fuzzy reasoning, the number of applications will only grow. Each element in this set of papers provides a solid theoretical foundation for the particular application of fuzzy reasoning, and then describes the specifics.
    There seems to be few limits that can be placed on the applications of expert systems, short of those mandated by the available hardware. However, like all disciplines, the quality of the results are defined by the theoretical foundations inherent in the code. The papers dealing with expert systems do an excellent job presenting the theory, but the emphasis in on the mathematics rather than the algorithms. The list of references is indicative of the recent developments of the fuzzy approach to expert systems, in that the majority are dated 1988 and later.
    The situation is somewhat different for neural networks. While they have proven to be very useful in some applications, many people remain skeptical regarding their use. Some of that comes from the name, which is considered a misnomer. With their many and medial levels, they are really nothing more than a multiple input minimization function made up by composing many sub-functions together in a specific way. That fact is the focus of all material on neural networks in this book. Very little is said about how to code implementations.
    The most interesting part of the book is the papers on computer vision and image analysis. What sparks the interest is the occasional listing of an algorithm. The theory, as always in this book, is well done, but algorithms give it life. It is clear that this area will continue to develop rapidly, and this is a very good introduction to the mathematical foundations of fuzzy image processing.
    This book is strongly recommended for those interested in the mathematics of fuzzy reasoning in expert systems, neural networks or computer vision. If your interest is in coding applications or the automating of this type of reasoning, this book is most likely not what you are looking for.

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