基于PS O参数优化的支持向量机齿轮箱故障诊断研究

摘要

为了解决支持向量机惩罚因子 c 和核函数 g的确定只能依靠先验知识的缺点，提出了基于粒子群优化算法（PSO）的支持向量机参数优化的模型，通过PSO的寻优自动获得最优的支持向量机参数。并运用JZQ250型齿轮箱进行故障诊断，实验表明所提出的模型很好地解决了参数选择问题，使SVM性能有所提升。%In order to solve the shortcoming of the support vector machine (SVM)'s penalty factor c and the nuclear function pa-rameter g only can depend upon the apriori knowledge,a model of SVM parameter optimization was presented,based on the optimal algorithm of PSO.Through automatic optimization of the PSO,the optimal parameters of SVMwere gotten,and the fault diagnosis was carried on by using the JZQ250 gear box.Experiments show that model presented is a very good solution to the problem of selecting pa-rameters,and makes some promotion of the SVM performance.