Computational Intelligence: A Methodological Introduction

✍ Scribed by Rudolf Kruse, Christian Borgelt, Christian Braune, Sanaz Mostaghim, Matthias Steinbrecher (auth.)

• Publisher: Springer-Verlag London
• Year: 2016
• Tongue: English
• Leaves: 556
• Series: Texts in Computer Science
• Edition: 2
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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

✦ Subjects

Artificial Intelligence (incl. Robotics);Appl.Mathematics/Computational Methods of Engineering