Computational Intelligence : Methods and Techniques

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). Publication of this book is a special event. This valuable title?lls a se- ous gap in domestic science and technical literature. At the same time it introduces a reader to the most recent achievements in the quickly dev- oping branch of knowledge which the computational intelligence has been for several years. The?eld, which is a subject of this book, is one of those important?elds of science which enable to process information included in data and give their reasonable interpretation programmed by a user. Recent decades have brought a stormy development of computer te- niquesandrelatedcomputationalmethods.Togetherwiththeirappearance and quick progress, theoretical and applied sciences developed as well, - ablingtheusertofullyutilizenewlycreatedcomputationalpotentialandto getknowledgeoutofincreasingwealthofdata.Thedevelopmentofcom- tational intelligence is then strictly connected with the increase of available data as well as capabilities of their processing, mutually supportive factors. Without them the development of this?eld would be almost impossible, and its application practically marginal. That is why these techniques have especially developed in recent years. The development of computational intelligence systems was inspired by observable and imitable aspects of intelligent activity of human being and nature. Nature when undertakes intelligent actions processes data in p- allel regulating and adjusting these actions through feedback mechanisms.

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 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. 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, including connections between these techniques and neural networks and fuzzy systems. Finally, various methods of data partitioning and algorithms of automatic data clustering are given and new neuro-fuzzy architectures are studied and compared. Introduction 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. Brand New International Paper-back Edition same as per description, **Economy edition, May have been printed in Asia with cover stating Not for sale in US. Legal to use despite any disclaimer on cover. Save Money. Contact us for any queries. Best Customer Support! All Orders shipped with Tracking Number.