【摘要】 针对惩罚因子C和核参数g选择不当造成支持向量机(SVM)分类效果不理想的问题,在基本粒子群(PSO)算法基础上引入动态惯性权重、全局邻域搜索、种群收缩因子、粒子变异概率等操作,提出了一种新的改进型粒子群(IPSO)算法优化SVM参数的分类器。采用Libsvm工具箱中的公共数据集BreastTissue,Heart和Wine来测试其分类效果,结果表明IPSO-SVM分类器在预测精度和分类时间上明显优于SVM和PSO-SVM分类器。然后将其应用于滚动轴承的二分类问题和多分类问题的故障诊断中,仿真实验证明IPSOSVM分类器能显著提高全局收敛能力和收敛速度,可得到理想的分类结果。最后,用IPSO-SVM分类器对实际轴承进行故障诊断,结果验证了其拥有良好的分类稳定性,值得进一步在工程领域内推广。更多还原

【Abstract】 Aiming at the problem that the classification effect of support vector machine(SVM)is not satisfactory due to improper selection of penalty factor Cand kernel parameter g,a new modified classifier that uses the improved particle swarm optimization(IPSO)was proposed to optimize the parameter of SVM(IPSO-SVM)by introducing the dynamic inertia weight,global neighborhood search,population shrinkage factor and particle mutation probability.The classification result was verified by common data sets named BreastTissue,Heart and Wine from the Libsvm toolbox,the results show that IPSO-SVM classifier is obviously superior to SVM and PSO-SVM classifier in terms of prediction accuracy and classification time.Then it was applied to the fault diagnosis in two classification problems and multiple classification problems of rolling bearings.The simulation results show that the IPSO-SVM classifier has stronger global convergence ability and faster convergence speed,and the ideal classification results can be obtained.Finally,the IPSO-SVM classifier was used to diagnose the fault of the actual bearing.The results show that the classifier has a better classification stability and is worthy of further promotion in engineering field.更多还原