System reliability forecasting by support vector machines with genetic algorithms

Authors:
Ping-Feng Pai
Affiliations:
Department of Information Management, National Chi Nan University, 1 University Road, Puli, Nantou, 545, Taiwan, ROC
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2006

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Abstract

Support vector machines (SVMs) have been used successfully to deal with nonlinear regression and time series problems. However, SVMs have rarely been applied to forecasting reliability. This investigation elucidates the feasibility of SVMs to forecast reliability. In addition, genetic algorithms (GAs) are applied to select the parameters of an SVM model. Numerical examples taken from the previous literature are used to demonstrate the performance of reliability forecasting. The experimental results reveal that the SVM model with genetic algorithms (SVMG) results in better predictions than the other methods. Hence, the proposed model is a proper alternative for forecasting system reliability. reliability.