中文摘要：

支持向量机是基于结构风险最小化原则的分类方法,其模型的分类准确度及泛化能力取决于惩罚系数、核函数及核参数的选取.参数的选择通常采用经验法、试凑法或单目标优化法,上述方法费时费力且不能达到全局最优.文中选取错误分类率及支持向量占有率两个目标函数,采用多目标粒子群算法优化惩罚系数及核函数参数.训练阶段采用多目标粒子群算法产生多个优化解,测试阶段评估这些解的优劣.通过齿轮故障分类实例进行了设计方法的验证,首先对振动信号进行预处理,然后以小波包系数标准差并降维处理后作为分类器的输入特征向量,最后用本文方法区分4种典型故障,试验结果表明了设计方法的有效性,并证明了应用混合核函数的支持向量机分类具备较高的准确率及较强的泛化性能.

英文摘要：

Support vector machine （SVM） is a classification method based on the structured risk minimization principle.The classification accuracy and generalization capability of SVM model depend on the selection of penalization coefficient,kernel function and kernel parameters.Currently,empirical method,error method and single-objective optimization are usually used in the process of parameters selection,which waste time and energy and cannot obtain the global optimal solution.In the paper,SVM misclassification rate and support vector occupation ratio are chosen as two objective functions,and SVM penalization coefficient and kernel parameters are optimized by multi-objective particle swarm optimization （MOPSO）.MOPSO is utilized to produce many optimization solutions in training stage,then these solutions will be evaluated in testing stage.The validation of design method is conducted by the case of the gear fault classification.Firstly,the vibration signals are preprocessed;secondly,the data after reducing dimensions of the standard deviation of wavelet package coefficients are regarded as the input eigenvector; finally,four typical faults of gear are classified.The results of test show that the method proposed is particularly valid and SVM classifier with mixed kernel function has higher accuracy and stronger generalization capability.