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Flexible Neuro-Fuzzy Systems: Structures, Learning and Performance Evaluation 2004 ed. [Kõva köide]

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Flexible Neuro-Fuzzy Systems: Structures, Learning and Performance Evaluation 2004 ed.Suurem pilt
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Püsilink: https://www.kriso.ee/db/9781402080425.html
Märksõnad:
Flexible Neuro-Fuzzy Systems is the first professional literature about the new class of powerful, flexible fuzzy systems. The author incorporates various flexibility parameters to the construction of neuro-fuzzy systems. This approach dramatically improves their performance, allowing the systems to perfectly represent the pattern encoded in data. Flexible Neuro-Fuzzy Systems is the only book that proposes a flexible approach to fuzzy modeling and fills the gap in existing literature. This book introduces new fuzzy systems which outperform previous approaches to system modeling and classification, and has the following features: -Provides a framework for unification, construction and development of neuro-fuzzy systems; -Presents complete algorithms in a systematic and structured fashion, facilitating understanding and implementation, -Covers not only advanced topics but also fundamentals of fuzzy sets, -Includes problems and exercises following each chapter, -Illustrates the results on a wide variety of simulations, -Provides tools for possible applications in business and economics, medicine and bioengineering, automatic control, robotics and civil engineering. Flexible Neuro-Fuzzy Systems is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a secondary text for graduate-level students in computer science and engineering.

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From the reviews:









"This research monograph offers a very detailed insight into flexible neurofuzzy architectures. The author systematically guides the reader through the essence of the neurofuzzy systems. Careful attention to details along with well-organized and systematic derivations of all learning formulas are the important features of the book. The wealth of experimental material and thorough comparative analysis is another strength of the book. The list of references is very much updated . Overall, the book is worth studying." (Witold Pedrycz, Zentralblatt MATH, Vol. 1080, 2006)



"It is fair to say that the work presented in this book moves the whole area of modeling by neuro-fuzzy systems to a higher level. a great resource for researchers in this area, who have a fairly strong background in both fuzzy systems and artificial neural networks. It is also an excellent textbook for advanced graduate students in engineering, systems science, computer science, information science, and perhaps some other areas, who are interested in neuro-fuzzy systems and, more generally, in applications of fuzzy mathematics." (George J. Klir, International Journal of General Systems, Vol. 34 (3), 2005)

FOREWORD xi
1. INTRODUCTION 1(6)
2. ELEMENTS OF THE THEORY OF FUZZY SETS 7(20)
2.1. Introduction
7(1)
2.2. Basic Definitions
7(6)
2.3. Triangular Norms and Negations
13(5)
2.4. Operations on Fuzzy Sets
18(3)
2.5. Fuzzy Relations
21(2)
2.6. Fuzzy Reasoning
23(2)
2.7. Problems
25(2)
3. FUZZY INFERENCE SYSTEMS 27(24)
3.1. Introduction
27(1)
3.2. Description of fuzzy inference systems
28(4)
3.3. Mamdani-type inference
32(5)
3.4. Logical-type inference
37(4)
3.5. Generalized neuro-fuzzy system
41(4)
3.6. Data sets used in the book
45(3)
3.7. Summary and discussion
48(1)
3.8. Problems
49(2)
4. FLEXIBILITY IN FUZZY SYSTEMS 51(24)
4.1. Introduction
51(1)
4.2. Weighted triangular norms
51(7)
4.3. Soft fuzzy norms
58(7)
4.4. Parameterized triangular norms
65(4)
4.5. OR-type systems
69(1)
4.6. Compromise systems
70(3)
4.7. Summary and discussion
73(1)
4.8. Problems
74(1)
5. FLEXIBLE OR-TYPE NEURO-FUZZY SYSTEMS 75(54)
5.1. Introduction
75(1)
5.2. Problem description
76(1)
5.3. Adjustable quasi-triangular norms
77(5)
5.4. Adjustable quasi-implications
82(4)
5.5. Basic flexible systems
86(4)
5.6. Soft flexible systems
90(9)
5.7. Weighted flexible systems
99(3)
5.8. Learning algorithms
102(13)
5.9. Simulation results
115(11)
5.10. Summary and discussion
126(1)
5.11. Problems
127(2)
6. FLEXIBLE COMPROMISE AND-TYPE NEURO-FUZZY SYSTEMS 129(36)
6.1. Introduction
129(1)
6.2. Problem description
130(1)
6.3. Basic compromise systems
130(3)
6.4. Soft compromise systems
133(7)
6.5. Weighted compromise systems
140(5)
6.6. Learning algorithms
145(6)
6.7. Simulation results
151(12)
6.8. Summary and discussion
163(1)
6.9. Problems
163(2)
7. FLEXIBLE MAMDANI-TYPE NEURO-FUZZY SYSTEMS 165(20)
7.1. Introduction
165(1)
7.2. Problem description
166(1)
7.3. Neuro-fuzzy structures
166(8)
7.4. Simulation results
174(9)
7.5. Summary and discussion
183(1)
7.6. Problems
183(2)
8. FLEXIBLE LOGICAL-TYPE NEURO-FUZZY SYSTEMS 185(50)
8.1. Introduction
185(1)
8.2. Problem description
185(1)
8.3. Neuro-fuzzy structures
186(22)
8.4. Simulation results
208(25)
8.5. Summary and discussion
233(1)
8.6. Problems
233(2)
9. PERFORMANCE COMPARISON OF NEURO-FUZZY SYSTEMS 235(20)
9.1. Introduction
235(1)
9.2. Comparison charts
236(15)
9.3. Summary and discussion
251(4)
APPENDIX 255(10)
BIBLIOGRAPHY 265(12)
INDEX 277