Flexible Neuro-fuzzy Systems

by Rutkowski, Leszek

• ISBN13: 9781402080425
• ISBN10: 1402080425
• Format: Hardcover
• Copyright: 2004-05-31
• Publisher: Kluwer Academic Pub

Summary

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.

Table of Contents

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