Computational Intelligence: A Methodological Introduction

by Rudolf Kruse, Christian Borgelt, Christian Braune, Sanaz Mostaghim, Matthias Steinbrecher

Overview

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 colonyoptimization and probabilistic graphical models.

Product Details

ISBN-13:	9781447173984
Publisher:	Springer London
Publication date:	06/09/2018
Series:	Texts in Computer Science
Edition description:	Softcover reprint of the original 2nd ed. 2016
Pages:	564
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

Rudolf Kruse is a full professor at the Department of Computer Science of the Otto-von-Guericke University of Magdeburg, Germany, where he leads the working group on computational intelligence. Christian Moewes and Pascal Held are research assistants at the same institution. Christian Borgelt is a principal researcher at the European Centre for Soft Computing, Mieres, Spain. Frank Klawonn is a Professor at the Department of Computer Science of Ostfalia University of Applied Sciences, Wolfenbüttel, Germany. Matthias Steinbrecher is a member of the SAP Innovation Center, Potsdam, Germany.

Table of Contents

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.

Editorial Reviews

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)

From the Publisher