Inhaltsverzeichnis
Introduction. - Part I: Neural Networks. - Introduction. - Threshold Logic Units. - General Neural Networks. - Multi-Layer Perceptrons. - Radial Basis Function Networks. - Self-Organizing Maps. - Hopfield Networks. - Recurrent Networks. - Mathematical Remarks for Neural Networks. - Part II: Evolutionary Algorithms. - Introduction to Evolutionary Algorithms. - Elements of Evolutionary Algorithms. - Fundamental Evolutionary Algorithms. - Computational Swarm Intelligence. - Part III: Fuzzy Systems. - Fuzzy Sets and Fuzzy Logic. - The Extension Principle. - Fuzzy Relations. - Similarity Relations. - Fuzzy Control. - Fuzzy Data Analysis. - Part IV: Bayes and Markov Networks. - Introduction to Bayes Networks. - Elements of Probability and Graph Theory. - Decompositions. - Evidence Propagation. - Learning Graphical Models. - Belief Revision. - Decision Graphs.
It is a great book, very well written, that presents solid content in a very rigorous theoretical and practical way and provides an excellent methodological guide to the area of computational intelligence one that could be qualified as a `must for the library of any student, professor, researcher or professional in that area. (José Luis Verdegay, Mathematical Reviews, May, 2017)
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