Boek
This book presents a variety of recently developed methods for generating fuzzyrules from data with the help of neural networks and evolutionary algorithms.Special efforts have been put on dealing with knowledge incorporation intoneural and evolutionary systems and knowledge extraction from data with thehelp of fuzzy logic. On the one hand knowledge that is understandable to humanbeings can be extracted from data using evolutionary and learning methods bymaintaining the interpretability of the generated fuzzy rules. On the otherhand a priori knowledge like expert knowledge and human preferences can beincorporated into evolution and learning taking advantage of the knowledgerepresentation capability of fuzzy rule systems and fuzzy preference models.Several engineering application examples in the fields of intelligent vehiclesystems process modeling and control and robotics are presented. TOCFuzzySets and Fuzzy Systems. Evolutionary Algorithms. Artificial Neural Networks.Conventional Datadriven Fuzzy Systems Design. Neural Network Based FuzzySystems Design. Evolutionary Design of Fuzzy Systems. Knowledge Discovery byExtracting Interpretable Fuzzy Rules. Fuzzy Knowledge Incorporation intoNeural Networks. Fuzzy Preferences Incorporation into Evolutionary Multiobjective Optimization. «
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