PSO-Based Evolutionary Learning

Ching-Yi Chen

ISBN 10: 3844331530 / ISBN 13: 9783844331530

Published by LAP LAMBERT Academic Publishing Mai 2011, 2011

About this Item

Neuware -The new paradigm of Swarm Intelligence, called Particle Swarm Optimization (PSO), is one of the well-known evolutionary computation techniques, which can be considered as an efficient tool to find near optimal solution in a searching space. Especially, PSO is a useful method when the problems to be solved are high-dimensional, nonlinear or some specific information is unavailable. PSO combines the social-only model and the cognition-only model to select the adjustable parameters to approach optimal solution, its main advantage is its rapid convergence and small computational requirements, which make it a good candidate for solving optimization problems. In this book, the efficient, robust, and flexible PSO algorithms are proposed to generate some artificial intelligence system in solving some applications, such as cluster analysis, image processing, and neural network training.Books on Demand GmbH, Überseering 33, 22297 Hamburg 156 pp. Englisch.

Seller Inventory # 9783844331530

Bibliographic Details

• Title: PSO-Based Evolutionary Learning
• Publisher: LAP LAMBERT Academic Publishing Mai 2011
• Publication Date: 2011
• Language: English
• ISBN 10: 3844331530
• ISBN 13: 9783844331530
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 10D millimeters 220 millimeters width by 150 millimeters height by 10 millimeters depth
• Weight: 250 grams

About this title

Synopsis

The new paradigm of Swarm Intelligence, called Particle Swarm Optimization (PSO), is one of the well-known evolutionary computation techniques, which can be considered as an efficient tool to find near optimal solution in a searching space. Especially, PSO is a useful method when the problems to be solved are high-dimensional, nonlinear or some specific information is unavailable. PSO combines the social-only model and the cognition-only model to select the adjustable parameters to approach optimal solution, its main advantage is its rapid convergence and small computational requirements, which make it a good candidate for solving optimization problems. In this book, the efficient, robust, and flexible PSO algorithms are proposed to generate some artificial intelligence system in solving some applications, such as cluster analysis, image processing, and neural network training.

About the Author

Ching-Yi Chen received his Ph.D. degree from Tamkang University, New Taipei City, Taiwan. He joined the Department of Information and Telecommunications, Ming-Chuan University in 2007 and is now an assistant professor. His main research interests include swarm intelligence, pattern recognition, and embedded systems.