Introduction to Neuro-Fuzzy Systems - Tapa blanda

Sinopsis

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.

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