【作者】 林平； 王福利； 刘浏；

【Author】 LIN Ping1,2,WANG Fu-li1,LIU Liu2 1.Key Laboratory of Integrated Automation of Process Industry,Ministry of Education,Northeastern University,Shenyang 110004 2.Metallurgical Department,General Iron&Steel Research Institute,Beijing 100081

【机构】 东北大学流程工业综合自动化教育部重点实验室； 钢铁研究总院冶金工艺研究所；

【摘要】 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.更多还原

【Abstract】 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.更多还原