This classroom-tested book discusses artificial neural networks, evolutionary algorithms, fuzzy systems and Bayesian networks, covering ant colony optimization and probabilistic graphical models. Includes examples, illustrations and definitions throughout.

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.

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 Includes supplementary material: sn.pub/extras

Autorentext

Dr. Rudolf Kruse is the former leader of the Computational Intelligence Research Group and now Emeritus Professor of the Department of Computer Science at the University of Magdeburg, Germany. Dr. Sanaz Mostaghim is a full Professor of Computer Science and Dr. Christian Braune is a Senior Lecturer at the same institution. Dr. Christian Borgelt is a Professor of Data Science at the Paris Lodron University of Salzburg, Austria. Dr. Matthias Steinbrecher is a Development Architect at SAP SE, Potsdam, Germany.

Klappentext

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.

Inhalt

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.