This book focuses on various techniques of computational intelligence, both single ones and those which form hybrid methods. Those techniques are today commonly applied issues of artificial intelligence, e.g. to process speech and natural language, build 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 various neural network architectures are presented and their learning algorithms are derived. Moreover, 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. This well-organized modern approach to methods and techniques of intelligent calculations includes examples and exercises in each chapter and a preface by Jacek Zurada, president of IEEE Computational Intelligence Society (2004-05).

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

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