Computational Intelligence: A Methodological Introduction (Texts in Computer Science)

Computational intelligence (CI) encompasses a range of nature-inspired methods that exhibit intelligent behavior in complex environments. This clearly-structured, classroom-tested textbook/reference presents a methodical introduction to the field of CI. Providing an authoritative insight into all that is necessary for the successful application of CI methods, the book describes fundamental concepts and their practical implementations, and explains the theoretical background underpinning proposed solutions to common problems. Only a basic knowledge of mathematics is required. Topics and features: Provides electronic supplementary material at an associated website, including module descriptions, lecture slides, exercises with solutions, and software tools Contains numerous examples and definitions throughout the text Presents self-contained discussions on artificial neural networks, evolutionary algorithms, fuzzy systems and Bayesian networks Covers the latest approaches, including ant colony optimization and probabilistic graphical models Written by a team of highly-regarded experts in CI, with extensive experience in both academia and industry Students of computer science will find the text a must-read reference for courses on artificial intelligence and intelligent systems. The book is also an ideal self-study resource for researchers and practitioners involved in all areas of CI 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 Part II: Evolutionary Algorithms Introduction to Evolutionary Algorithms Elements of Evolutionary Algorithms Fundamental Evolutionary Algorithms Special Applications and Techniques Part III: Fuzzy Systems Fuzzy Sets and Fuzzy Logic The Extension Principle Fuzzy Relations Similarity Relations Fuzzy Control Fuzzy Clustering Part IV: Bayes Networks Introduction to Bayes Networks Elements of Probability and Graph Theory Decompositions Evidence Propagation Learning Graphical Models