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The primary purpose of this book is to present information about selected topics on the interactions and applications of fuzzy + neural. Most of the discussion centers around our own research in these areas. Fuzzy + neural can mean many things: (1) approximations between fuzzy systems and neu ral nets (Chapter 4); (2) building hybrid neural nets to equal fuzzy systems (Chapter 5); (3) using neura.l nets to solve fuzzy problems (Chapter 6); (4) approximations between fuzzy neural nets and other fuzzy systems (Chap ter 8); (5) constructing hybrid fuzzy neural nets for certain fuzzy systems (Chapters 9, 10); or (6) computing with words (Chapter 11). This book is not intend to be used primarily as a text book for a course in fuzzy + neural because we have not included problems at the end of each chapter, we have omitted most proofs (given in the references), and we have given very few references. We wanted to keep the mathematical prerequisites to a minimum so all longer, involved, proofs were omitted. Elementary dif ferential calculus is the only prerequisite needed since we do mention partial derivatives once or twice.
List of contents
1. Introduction.- 2. Fuzzy Sets and Fuzzy Functions.- 3. Neural Nets.- 4. First Approximation Results.- 5. Hybrid Neural Nets.- 6. Neural Nets Solve Fuzzy Problems.- 7. Fuzzy Neural Nets.- 8. Second Approximation Results.- 9. Hybrid Fuzzy Neural Nets.- 10. Applications of Hybrid Fuzzy Neural Nets and Fuzzy Neural Nets.- 11. Fuzzy Teaching Machine.- 12. Summary, Future Research and Conclusions.
