Table of Contents

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.

About the Author

Rudolf Kruse and Sanaz Mostaghim are professors at the Department of Computer Science of the Otto von Guericke University of Magdeburg, Germany. Christian Borgelt is a principal researcher, and Christian Braune is a research assistant at the same institution. Matthias Steinbrecher is with SAP SE, Potsdam, Germany.

Reviews

"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)