This textbook provides a clear and logical introduction to the field, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. This enhanced second edition has been fully revised and expanded with new content on swarm intelligence, deep learning, fuzzy data analysis, and discrete decision graphs.
Features: provides supplementary material at an associated website; contains numerous classroom-tested examples and definitions throughout the text; presents useful insights into all that is necessary for the successful application of computational intelligence methods; explains the theoretical background underpinning proposed solutions to common problems; discusses in great detail the classical areas of artificial neural networks, fuzzy systems and evolutionary algorithms; reviews the latest developments in the field, covering such topics as ant colony optimization and probabilistic graphical models.
Arvustused
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)
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.
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.
