Support Vector Machines and Particle Swarm Optimization
Автор:Isis Didier Lins,Marcio das Chagas Moura and Enrique Lopez Droguett Год: 2010 Издание:LAP Lambert Academic Publishing Страниц: 92 ISBN: 9783838319407 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...
