Intelligent Hybrid Systems, Fuzzy Logic, Neural Networks, And Genetic Algorithms

Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms is an organized edited collection of contributed chapters covering basic principles, methodologies, and applications of fuzzy systems, neural networks and genetic algorithms. All chapters are original contributions by leading researchers written exclusively for this volume.
This book reviews important concepts and models, and focuses on specific methodologies common to fuzzy systems, neural networks and evolutionary computation. The emphasis is on development of cooperative models of hybrid systems. Included are applications related to intelligent data analysis, process analysis, intelligent adaptive information systems, systems identification, nonlinear systems, power and water system design, and many others.
Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms provides researchers and engineers with up-to-date coverage of new results, methodologies and applications for building intelligent systems capable of solving large-scale problems.

Booknews

Twenty-nine global contributors attest to the facts that the methodologies of fuzzy logic, neural networks, and genetic algorithms are revolutionary, biologically inspired, capable of solving nonlinear problems, and most powerful as approximate-reasoning frameworks when integrated into hybrid models. Basic principles and methodologies are overviewed for system engineering researchers as well as cognitive scientists, as are the data analysis and information systems pivotal to intelligent system applications, and the genetic programming and design challenges of complex nonlinear systems and system identification. Annotation c. by Book News, Inc., Portland, Or.