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.
The authors have written Computational intelligence in such a way that it can serve as both a textbook and a helpful reference book for students and practitioners of computing science and related fields. The presentation is careful and friendly yet technically sound. (Soubhik Chakraborty, Computing Reviews, March 7, 2023)
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