Computational intelligence : a methodological introduction / Rudolf Kruse...[et al.].
Material type:
TextSeries: Texts in computer sciencePublication details: London : Springer-Verlag, 2013.Description: xi, 490 p. ; illustrationsISBN: - 9781447150121 (hard cover : alk. paper)
- 23 K94 006.3
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 006.3 K94 (Browse shelf(Opens below)) | Available | 135638 |
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| 006.3 K82 Computational intelligence | 006.3 K89 Self-adaptive heuristics for evolutionary computation | 006.3 K94 Artificial reality | 006.3 K94 Computational intelligence : | 006.3 K96 Age of intelligent machines | 006.3 K96 Fuzzy classifier design | 006.3 K96 On growth, form and computers |
Includes bibliographical references and index.
Part I. Neural Networks.
2. Introduction --
3. Threshold Logic Units --
4. General Neural Networks --
5. Multi-Layer Perceptrons --
6. Radial Basis Function Networks --
7. Self-organizing Maps --
8. Hopfield Networks --
9. Recurrent Networks --
10. Mathematical Remarks --
Part II. Evolutionary Algorithms.
11. Introduction to Evolutionary Algorithms --
12. Elements of Evolutionary Algorithms --
13. Fundamental Evolutionary Algorithms --
14. Special Applications and Techniques --
Part III. Fuzzy Systems.
15.Fuzzy Sets and Fuzzy Logic --
16. The Extension Principle --
17. Fuzzy Relations --
18. Similarity Relations --
19. Fuzzy Control --
20. Fuzzy Clustering --
Part IV. Bayes Networks.
21. Introduction to Bayes Networks --
22. Elements of Probability and Graph Theory --
23. Decompositions --
24. Evidence Propagation --
25. Learning Graphical Models--
References--
Index.
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 toolsContains numerous examples and definitions throughout the textPresents self-contained discussions on artificial neural networks, evolutionary algorithms, fuzzy systems and Bayesian networksCovers the latest approaches, including ant colony optimization and probabilistic graphical modelsWritten by a team of highly-regarded experts in CI, with extensive experience in both academia and industryStudents 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.
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