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Computational Intelligence A Methodological Introduction

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Beschreibung

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,...

Beschreibung

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.

Rezension

From the reviews:

“This book teaches computational intelligence (CI) in a thorough, methodological manner that is theoretically profound and educationally oriented. … this book is well designed for the independent student who wishes to learn the fundamentals of CI without the need for an instructor. The organization and thorough step-by-step methodology makes it an excellent startup guide for someone who wants to learn CI … . This book is targeted at beginners, students, or professionals who wish to understand CI.” (Mario Antoine Aoun, Computing Reviews, February, 2014)

“The book under review is a textbook that features sub-symbolic approaches developed within the field of Artificial Intelligence … . It can be used as a companion book for lectures, with exercises and slides to be found on the book’s website. With its focus on sub-symbolic approaches, it presents a comprehensive and detailled source of information complementary to other commonly used textbooks in Artificial Intelligence that mostly focus on symbolic approaches.” (Jana Köhler, zbMATH, Vol. 1283, 2014)

“The book is a comprehensive treatise on computational intelligence with a focus on the underlying methodology and algorithms. … The reader can enjoy a comprehensive and systematically arranged exposure of the material. … The references following each chapter can serve as a list of introductory readings on the individual areas of computational intelligence. … the reader gains a good sense of computational intelligence as an important endeavor supporting analysis and synthesis of intelligent systems. … a useful compendium of knowledge for a broad audience.” (Witold Pedrycz, Mathematical Reviews, November, 2013)

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

08.02.2015

Abbildungen

XII, 492 p.

Verlag Springer London

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

08.02.2015

Abbildungen

XII, 492 p.

Verlag Springer London
Seitenzahl

492

Maße (L/B/H)

23.5/15.5/2.8 cm

Gewicht

756 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-4471-5849-3

Herstelleradresse

Springer Nature Customer Service Center GmbH
Europaplatz 3
69115 Heidelberg
DE
ProductSafety@springernature.com

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  • Produktbild: Computational Intelligence
  • Produktbild: Computational Intelligence

  • 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.- Part II: Evolutionary Algorithms.
    -
    Introduction to Evolutionary Algorithms.- Elements of Evolutionary Algorithms.- Fundamental Evolutionary Algorithms.- Special Applications and Techniques.- Part III: Fuzzy Systems.
    -
    Fuzzy Sets and Fuzzy Logic.- The Extension Principle.- Fuzzy Relations.- Similarity Relations.- Fuzzy Control.- Fuzzy Clustering.- Part IV: Bayes Networks.
    -
    Introduction to Bayes Networks.- Elements of Probability and Graph Theory.- Decompositions.- Evidence Propagation.- Learning Graphical Models.