Computational Intelligence in Reliability Engineering

ISBN 10: 3540373675 / ISBN 13: 9783540373674

Published by Springer, 2006

About this Item

pp. 414 Illus.

Seller Inventory # 7545186

Bibliographic Details

• Title: Computational Intelligence in Reliability Engineering
• Publisher: Springer
• Publication Date: 2006
• Language: English
• ISBN 10: 3540373675
• ISBN 13: 9783540373674
• Binding: Hardcover
• Condition: New

About this title

Synopsis

This two-volume book covers the recent applications of computational intel- gence techniques in reliability engineering. Research in the area of computational intelligence is growing rapidly due to the many successful applications of these new techniques in very diverse problems. “Computational Intelligence” covers many fields such as neural networks, fuzzy logic, evolutionary computing, and their hybrids and derivatives. Many industries have benefited from adopting this technology. The increased number of patents and diverse range of products dev- oped using computational intelligence methods is evidence of this fact. These techniques have attracted increasing attention in recent years for solving many complex problems. They are inspired by nature, biology, statistical te- niques, physics and neuroscience. They have been successfully applied in solving many complex problems where traditional problem-solving methods have failed. The book aims to be a repository for the current and cutting-edge applications of computational intelligent techniques in reliability analysis and optimization.

From the Back Cover

This book covers the recent applications of computational intelligence techniques in reliability engineering. This volume contains a survey of the contributions made to the optimal reliability design literature in the resent years and chapters devoted to different applications of a genetic algorithm in reliability engineering and to combinations of this algorithm with other computational intelligence techniques. Genetic algorithms are one of the most widely used metaheuristics, inspired by the optimization procedure that exists in nature, the biological phenomenon of evolution.