Fuzzy Logic With Engineering Appl 2e / Edition 2by Timothy J. Ross
Pub. Date: 07/21/2004
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… See more details below
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.
- Publication date:
- Edition description:
- Older Edition
- Product dimensions:
- 1.38(w) x 6.69(h) x 9.61(d)
Table of Contents
About the Author.
Preface to the Second Edition.
2. Classical Sets and Fuzzy Sets.
3. Classical Relations and Fuzzy Relations.
4. Properties of Membership Functions, Fuzzification, and Defuzzification.
5. Logic and Fuzzy Systems.
6. Development of Membership Functions.
7. Automated Methods for Fuzzy Systems.
8. Fuzzy Systems Simulation.
9. Rule-base Reduction Methods.
10. Decision Making with Fuzzy Information.
11. Fuzzy Classification and Pattern Recognition.
12. Fuzzy Arithmetic and the Extension Principle.
13. Fuzzy Control Systems.
14. Miscellaneous Topics.
15. Monotone Measures: Belief, Plausibility, Probability, and Possibility.
Appendix A: Axiomatic Differences between Fuzzy Set Theory and Probability Theory.
Appendix B: Answers to Selected Problems.
Index of Examples and Problems by Discipline.
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