Shengchang Wang

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Accurate reliability prediction is important for assessing product performances and making maintenance plans. This research applies least square support vector machine (LSSVM) in reliability analysis of engine systems. To evaluate the predictive performance of LSSVM, a comparative study is made and the probability distribution of the forecasting outcomes is(More)
Aiming at features of strong randomicity, complexity and nonlinearity in highway freight volume, two forecasting models based on support vector machine (SVM) and least squares support vector machine (LSSVM) are proposed. Comparative research and numerical calculation on these two models shows that the forecasting precise based on SVM is better than LSSVM's,(More)
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