Recurrent support vector machines in reliability prediction

Authors:
Wei-Chiang Hong;Ping-Feng Pai;Chen-Tung Chen;Ping-Teng Chang
Affiliations:
School of Management, Da-Yeh University, Da-Tusen, Chang-hua, Taiwan;Department of Information Management, National Chi Nan University, Nantou, Taiwan;Department of Information Management, Da-Yeh University, Da-Tusen, Chang-hua, Taiwan;Department of Industrial Engineering and Enterprise Information, Tunghai University, Tunghai University, Taichung, Taiwan
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

Support vector machines (SVMs) have been successfully used in solving nonlinear regression and times series problems. However, the application of SVMs for reliability prediction is not widely explored. Traditionally, the recurrent neural networks are trained by the back-propagation algorithms. In the study, SVM learning algorithms are applied to the recurrent neural networks to predict system reliability. In addition, the parameter selection of SVM model is provided by Genetic Algorithms (GAs). A numerical example in an existing literature is used to compare the prediction performance. Empirical results indicate that the proposed model performs better than the other existing approaches.