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Computational Intelligence (2nd Edition)

A Methodological Introduction

Author(s):
Publisher:

Springer

Pages: 564
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Recommend to library

AVAILABLE FORMATS

Paperback - 9781447173984

08 June 2018

$74.99

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Hardcover - 9781447172949

26 September 2016

$89.99

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Ebook - 9781447172963

16 September 2016

$59.99

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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...

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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.

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Written by a team of highly-regarded experts, with extensive experience in both academia and industry

Offers a profound theoretical introduction to computational intelligence for both students and practitioners

Provides electronic supplementary material at an associated website, including module descriptions, lecture slides, exercises with solutions, and software tools

Fully revised new edition, expanded with new content on swarm intelligence, deep learning, fuzzy data analysis, and discrete decision graphs

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.

“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)
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Rudolf Kruse and Sanaz Mostaghim are professors at the Department of Computer Science of the Otto von Guericke University of Magdeburg, Germany. Christian Borgelt is a principal researcher, and Christian Braune is a research assistant at the same institution. Matthias Steinbrecher is with SAP SE, Potsdam, Germany.

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Rudolf Kruse and Sanaz Mostaghim are professors at the Department of Computer Science of the Otto von Guericke University of Magdeburg, Germany. Christian Borgelt is a principal researcher, and Christian Braune is a research assistant at the same institution. Matthias Steinbrecher is with SAP SE, Potsdam, Germany.

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