Computational Intelligence

This brilliant new book focuses on various techniques of computational intelligence, both single ones and those which form hybrid methods. These techniques are applied to issues of artificial intelligence, for example in the processing of speech and natural language, and in building expert systems and robots.

This quite simply superb book focuses on various techniques of computational intelligence, both single ones and those which form hybrid methods. These techniques are today commonly applied to issues of artificial intelligence, for example in the processing of speech and natural language, and in building expert systems and robots. The first part of the book presents methods of knowledge representation using different techniques, namely the rough sets, type-1 fuzzy sets and type-2 fuzzy sets. Next up, various neural network architectures are presented and their learning algorithms are derived. Then, the family of evolutionary algorithms is discussed, in particular the classical genetic algorithm, evolutionary strategies and genetic programming, including connections between these techniques and neural networks and fuzzy systems. In the last part of the book, various methods of data partitioning and algorithms of automatic data clustering are given and new neuro-fuzzy architectures are studied and compared.

Self-contained, easy accessible and comprehensive textbook for graduate students and researchers in computational intelligence Well-organized modern approach to methods and techniques of intelligent calculations including examples and exercises in each chapter Includes a preface by Jacek Zurada, president of IEEE Computational Intelligence Society Rich and well structured compendium of information about computational intelligence Topics include approximate and fuzzy sets, basic structures and methods of neural networks learning, grouping of data methods, Bayesian methods, evolutionary algorithms and decision tree algorithms

Inhalt
Selected issues of artificial intelligence.- Methods of knowledge representation using rough sets.- Methods of knowledge representation using type-1 fuzzy sets.- Methods of knowledge representation using type-2 fuzzy sets.- Neural networks and their learning algorithms.- Evolutionary algorithms.- Data clustering methods.- Neuro-fuzzy systems of Mamdani, logical and Takagi-Sugeno type.- Flexible neuro-fuzzy systems.