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System reliability forecasting by support vector machines with genetic algorithms. (English) Zbl 1187.90113

Summary: 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.

MSC:

90B25 Reliability, availability, maintenance, inspection in operations research
90C59 Approximation methods and heuristics in mathematical programming
68T05 Learning and adaptive systems in artificial intelligence
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