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Computational Intelligence : A Methodological Introduction / by Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, Pascal Held.
- Format:
- Book
- Author/Creator:
- Kruse, Rudolf, author.
- Borgelt, Christian, author.
- Klawonn, F., author.
- Moewes, Christian, author.
- Steinbrecher, Matthias, author.
- Held, Pascal, author.
- Series:
- Computer Science (Springer-11645)
- Texts in computer science 1868-0941
- Texts in Computer Science, 1868-0941
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Applied mathematics.
- Engineering mathematics.
- Artificial Intelligence.
- Mathematical and Computational Engineering.
- Local Subjects:
- Artificial Intelligence.
- Mathematical and Computational Engineering.
- Physical Description:
- 1 online resource (XII, 492 pages).
- Edition:
- First edition 2013.
- Contained In:
- Springer eBooks
- Place of Publication:
- London : Springer London : Imprint: Springer, 2013.
- System Details:
- text file PDF
- Summary:
- Computational intelligence (CI) encompasses a range of nature-inspired methods that exhibit intelligent behavior in complex environments. 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. Topics and 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 Students of computer science will find the text a must-read reference for courses on artificial intelligence and intelligent systems. The book is also an ideal self-study resource for researchers and practitioners involved in all areas of CI.
- 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
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-1-4471-5013-8
- 9781447150138
- Access Restriction:
- Restricted for use by site license.
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