Fuzzy logic refers to a large subject dealing with a set of methods to characterize and quantify uncertainty in engineering systems that arise from ambiguity, imprecision, fuzziness, and lack of knowledge. Fuzzy logic is a reasoning system based on a foundation of fuzzy set theory, itself an extension of classical set theory, where set membership can be partial as opposed to all or none, as in the binary features of classical logic.
Fuzzy logic is a relatively new discipline in which major advances have been made over the last decade or so with regard to theory and applications. Following on from the successful first edition, this fully updated new edition is therefore very timely and much anticipated. Concentration on the topics of fuzzy logic combined with an abundance of worked examples, chapter problems and commercial case studies is designed to help motivate a mainstream engineering audience, and the book is further strengthened by the inclusion of an online solutions manual as well as dedicated software codes.
Senior undergraduate and postgraduate students in most engineering disciplines, academics and practicing engineers, plus some working in economics, control theory, operational research etc, will all find this a valuable addition to their bookshelves.
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Fuzzy logic is a simple phrase that actually refers to a large subject dealing with a set of methods to characterize and quantify uncertainty in engineering systems that arise from ambiguity, imprecision, fuzziness, and lack of knowledge.
This 15-chapter textbook remains the only major text that can be used for both undergraduate and graduate classroom instruction in this technology field. The first 8 chapters of the text cover fundamental materials useful in characterizing various forms of uncertainty and in developing the methods to quantify these uncertainties. Four chapters present specific case studies in decision making, classification and pattern recognition, control, simulation, and fuzzy arithmetic. There is one chapter on miscellaneous applications of fuzzy logic, one chapter on new rule-reduction techniques, and the final chapter presents material on other uncertainty theories with examples using evidence theory, possibility theory, and probability theory.
Key features include:
This book will appeal to senior undergraduate and graduate students in engineering, and to graduate students in many other scientific fields. Practitioners in control theory, classification, systems integration and systems modeling and operations research application will also find this a useful text.About the Author:
Timothy J. Ross is Professor and Regents’ Lecturer of Civil Engineering at the University of New Mexico. He received his PhD degree in Civil Engineering from Stanford University, his MS from Rice University, and his BS from Washington State University. Professor Ross has held previous positions as Senior Research Structural Engineer, Air Force Weapons Laboratory, from 1978 to 1986; and Vulnerability Engineer, Defense Intelligence Agency, from 1973 to 1978. Professor Ross has authored more than 120 publications and has been active in the research and teaching of fuzzy logic since 1983. He is the founding Co-Editor-in-Chief of the International Journal of Intelligent and Fuzzy Systems and the co-editor of Fuzzy Logic and Control: Software and Hardware Applications, and most recently co-editor of Fuzzy Logic and Probability Applications: Bridging the Gap. Professor Ross is a Fellow of the American Society of Civil Engineers. He consults for industry and such institutions as Sandia National Laboratory and the National Technological University, and is a current Faculty Affiliate with the Los Alamos National Laboratory. He was recently honored with a Senior Fulbright Fellowship for his sabbatical study at the University of Calgary, Alberta, Canada.
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