Computational Intelligence: A Methodological Introduction

Description

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. Front Matter....Pages i-xiii Introduction to Computational Intelligence....Pages 1-5 Front Matter....Pages 7-7 Introduction to Neural Networks....Pages 9-13 Threshold Logic Units....Pages 15-35 General Neural Networks....Pages 37-46 Multilayer Perceptrons....Pages 47-92 Radial Basis Function Networks....Pages 93-112 Self-organizing Maps....Pages 113-129 Hopfield Networks....Pages 131-157 Recurrent Networks....Pages 159-171 Mathematical Remarks for Neural Networks....Pages 173-180 Front Matter....Pages 181-181 Introduction to Evolutionary Algorithms....Pages 183-212 Elements of Evolutionary Algorithms....Pages 213-243 Fundamental Evolutionary Algorithms....Pages 245-297 Computational Swarm Intelligence....Pages 299-325 Front Matter....Pages 327-327 Introduction to Fuzzy Sets and Fuzzy Logic....Pages 329-359 The Extension Principle....Pages 361-367 Fuzzy Relations....Pages 369-382 Similarity Relations....Pages 383-393 Fuzzy Control....Pages 395-430 Fuzzy Data Analysis....Pages 431-456 Front Matter....Pages 457-457 Introduction to Bayes Networks....Pages 459-463 Elements of Probability and Graph Theory....Pages 465-491 Decompositions....Pages 493-505 Evidence Propagation....Pages 507-519 Learning Graphical Models....Pages 521-530 Belief Revision....Pages 531-539 Decision Graphs....Pages 541-551 Back Matter....Pages 553-564