<p>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 implementatio
Computational Intelligence: a Methodological Introduction
β Scribed by Kruse, Rudolf
- Publisher
- Springer London
- Year
- 2013
- Tongue
- English
- Leaves
- 482
- Series
- Texts in Computer Science
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
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.
β¦ 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 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
β¦ Subjects
Soft Computing;Artificial intelligence;Computer science;Engineering mathematics
π SIMILAR VOLUMES
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 third edition has been fully revised and expand
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 behaviorΒ in complex environments. This enhanced second edition has been fully revised and expan
<p>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 ex