粒子群优化核极限学习机的变压器故障诊断
裴飞;陈雪振;朱永利;遇炳杰
【期刊名称】《计算机工程与设计》 【年(卷),期】2015(000)005
【摘要】The kernel-based extreme learning machine (KELM)has better classification performance than the SVM,but it still has the drawback of parameter sensitivity.For this defect,a method combining K-fold cross validation and particle swarm optimization (PSO)was proposed to optimize the parameter of KELM classi-fier,the average accuracy rate of the multi-ple models generated using the CV method was used as the fitness function of PSO to provide an evaluation criteria of KELM classifier.And this proposed method was used in the transformer fault diagnosis to make full use of the limited number of date samples and improve the generalization performance of KELM.Experimental result show that comparing with the method of KELM based on grid search,KELM based on CV and grid search and KELM based PSO,the proposed method has better per-formance.%核极限学习机（kernel-based extreme learning machine，KELM）在分类性能方面优于支持向量机（SVM），但仍存在参数敏感性的缺陷。针对这一缺陷，提出一种结合K 折交叉验证（k-fold cross validation，K-CV）与粒子群优化（particle swarm optimization，PSO）的KELM分类器参数优化方法，将CV训练所得多个模型的平均准确率作为PSO的适应度评价函数，为KELM的参数优化提供评价
粒子群优化核极限学习机的变压器故障诊断
