针对齿轮钢精炼过程中钢材淬透性难以预测的问题，本文提出了一种多支持向量机的建模方法。将影响淬透性的各因素按其相关性进行分类，根据分类结果确定子模型个数和子模型的输入. 同时，为保证模型具有更好的拟合精度和泛化能力，在模型的训练中采用遗传算法对支持向量机进行参数寻优. 仿真结果表明，采用多支持向量机建立的钢材淬透性预测模型具有更高的预测精度.
Hardenability prediction is very difficult in the steel refining process. Based on the idea that the accuracy of model can be significantly improved by combining several sub-models, a multiple support vector machine(MSVM) based hardenability prediction model is proposed in this paper. The influence factors of hardenability are analysised to determines the number of sub-model and the input variables of the sub-model. In order to improve the precision and generalization capability of the prediction model, genetic algorithm (GA) is adopted to optimize the parameters of MSVM. The simulation results demonstrate the efficiency of the method.
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