Introduction to Neuro-Fuzzy Systems

Robert Fuller

ISBN 10: 3790812560 / ISBN 13: 9783790812565

Editore: Physica-Verlag HD, 1999

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Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.

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Dati bibliografici

• Titolo: Introduction to Neuro-Fuzzy Systems
• Editore: Physica-Verlag HD
• Data di pubblicazione: 1999
• Lingua: Inglese
• ISBN 10: 3790812560
• ISBN 13: 9783790812565
• Rilegatura: Brossura
• Condizione: Sehr gut

Descrizione articolo

Riassunto

This book contains introductory material to neuro-fuzzy systems. Its main purpose is to explain the information processing in mostly-used fuzzy inference systems, neural networks and neuro-fuzzy systems. More than 180 figures and a large number of (numerical) exercises (with solutions) have been inserted to explain the principles of fuzzy, neural and neuro-fuzzy systems. Also the mathematics applied in the models is carefully explained, and in many cases exact computational formulas have been derived for the rules in error correction learning procedures.
Numerous models treated in the book will help the reader to design his own neuro-fuzzy system for his specific (managerial, industrial, financial) problem. The book can serve as a textbook for students in computer and management sciences who are interested in adaptive technologies.

Contenuti

Fuzzy Systems: An introduction to fuzzy logic; Operations on fuzzy sets; Fuzzy relations; The extension principle; The extension principle for n-place functions; Metrics for fuzzy numbers; Measures of possibility and necessity; Fuzzy implications; Linguistic variables; The theory of approximate reasoning; An introduction to fuzzy logic controllers; Defuzzification methods; Inference mechanisms; Construction of data base and rule base of FLC; The ball and beam problem; Aggregation in fuzzy system modeling; Averaging operators; Fuzzy screening systems; Applications of fuzzy systems.- Artificial Neural Networks: The perceptron learning rule; The delta learning rule; The delta learning rule with semilinear activation function; The generalized delta learning rule; Effectivity of neural networks; Winner-take-all-learning; Applications of artificial neural networks.- Fuzzy Neural Networks: Integration of fuzzy logic and neural networks; Fuzzy neurons; Hybrid neural nets; Computation of fuzzy logic inferences by hybrid neural net; Trainable neural nets for fuzzy IF-THEN rules; Implementation of fuzzy rules by regular FNN of Type 2; Implementation of fuzzy rules by regular FNN of Type 3; Tuning fuzzy control parameters by neural nets; Fuzzy rule extraction from numerical data; Neuro-fuzzy classifiers; FULLINS; Applications of fuzzy neural systems.- Appendix: Case study: A portfolio problem; Exercises.