Support Vector Machines and Particle Swarm Optimization

Das Chagas Moura Mrcio Lpez Droguett Enrique Lins Isis Didier

ISBN 10: 3838319400 / ISBN 13: 9783838319407

Published by VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010

About this Item

Print on Demand pp. 92 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Seller Inventory # 131703631

Bibliographic Details

• Title: Support Vector Machines and Particle Swarm Optimization
• Publisher: VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG
• Publication Date: 2010
• Language: English
• ISBN 10: 3838319400
• ISBN 13: 9783838319407
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Reliability is a critical indicator of organizations' performance in face of market competition, since it contributes to production regularity. Its prediction is of great interest as it may anticipate trends of system failures and thus enable maintenance actions. The consideration of all aspects that influence system reliability may render its modeling very complex and learning methods such as Support Vector Machines (SVMs) emerge as alternative prediction tools: previous knowledge about the function or process that maps input variables into output is not required. However, SVM performance is affected by parameters from the related learning problem. Suitable values for them are chosen by means of Particle Swarm Optimization (PSO), a probabilistic approach based on the behavior of organisms that move in groups. Thus, a PSO+SVM methodology is proposed to handle reliability prediction problems. It is used to solve application examples based on time series data and also involving data collected from oil production wells. The results indicate that PSO+SVM is able to provide competitive or even more accurate reliability predictions when compared, for example, to Neural Networks (NNs).