摘要
采用合成核函数构造支持向量机模型,运用粒子群优化算法(PSO)对模型参数进行参数寻优,利用UCI数据集的数据进行分类验证。与单核SVM相比,该方法具有更好的分类能力和运算速度。将合成核SVM应用到风机齿轮箱的故障诊断中,取得了良好的效果。
The support vector machines （SVM） is a kind of novel statistics learning method.In the case of small samples,SVM has a higher classification capability.In this paper,SVM with composite kernels will be adopted to optimize the parameters of SVM with the particle swarm optimization （PSO） and to carry out the experiments on the UCI database.Compared with the single kernel SVM,the composite kernels can improve precision of classification and get a better classification performance.Finally,we use SVM with composite kernels in the fault diagnosis of wind power gearbox and have a good result.
出处
《江南大学学报（自然科学版）》
CAS
2013年第5期607-612,共6页
Joural of Jiangnan University (Natural Science Edition)
基金
上海市教委重点学科项目(J51901)
上海市科委"科技创新行动计划"重大科技项目(11DZ1200207)
上海市教育委员会科研创新项目(13YZ139)
