Support Vector Machines and Particle Swarm Optimization | Applications to Reliability Prediction

Isis Didier Lins (u. a.)

ISBN 10: 3838319400 / ISBN 13: 9783838319407

Published by LAP LAMBERT Academic Publishing, 2010

About this Item

Support Vector Machines and Particle Swarm Optimization | Applications to Reliability Prediction | Isis Didier Lins (u. a.) | Taschenbuch | 92 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838319407 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 101312756

Bibliographic Details

• Title: Support Vector Machines and Particle Swarm Optimization | Applications to Reliability Prediction
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2010
• Language: English
• ISBN 10: 3838319400
• ISBN 13: 9783838319407
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 7D millimeters 220 millimeters width by 150 millimeters height by 7 millimeters depth
• Weight: 155 grams
• Seller catalogs:
  • Bücher

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).