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. 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.
Author(s): Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, Pascal Held (auth.)
Series: Texts in Computer Science
Edition: 1
Publisher: Springer-Verlag London
Year: 2013
Language: English
Pages: 492
Tags: Artificial Intelligence (incl. Robotics); Appl.Mathematics/Computational Methods of Engineering
Front Matter....Pages I-XI
Introduction....Pages 1-5
Front Matter....Pages 7-7
Introduction....Pages 9-13
Threshold Logic Units....Pages 15-35
General Neural Networks....Pages 37-46
Multi-Layer Perceptrons....Pages 47-81
Radial Basis Function Networks....Pages 83-103
Self-organizing Maps....Pages 105-120
Hopfield Networks....Pages 121-142
Recurrent Networks....Pages 143-155
Mathematical Remarks....Pages 157-164
Front Matter....Pages 165-165
Introduction to Evolutionary Algorithms....Pages 167-195
Elements of Evolutionary Algorithms....Pages 197-226
Fundamental Evolutionary Algorithms....Pages 227-274
Special Applications and Techniques....Pages 275-291
Front Matter....Pages 293-293
Fuzzy Sets and Fuzzy Logic....Pages 295-319
The Extension Principle....Pages 321-327
Fuzzy Relations....Pages 329-340
Similarity Relations....Pages 341-352
Fuzzy Control....Pages 353-387
Fuzzy Clustering....Pages 389-405
Front Matter....Pages 407-407
Introduction to Bayes Networks....Pages 409-413
Elements of Probability and Graph Theory....Pages 415-440
Decompositions....Pages 441-454
Evidence Propagation....Pages 455-468
Learning Graphical Models....Pages 469-477
Back Matter....Pages 479-490
