基于粒子群支持向量机的轨道电路故障诊断

Track circuit fault diagnosis based on particle swarm optimization and support vector machine

摘要: 支持向量机（SVM）是一种解决小样本分类问题的最佳理论算法，它的核函数的参数选择非常重要，直接影响着故障诊断的准确率。本文将粒子群算法（PSO）用于支持向量机的参数优化，提出基于粒子群支持向量机的故障诊断模型，并将其运用于轨道电路中。通过对比MATLAB仿真结果得出：经过粒子群寻优得到的参数比随机选取的参数更优，所建立的PSO-SVM模型的故障诊断准确率高于普通的SVM模型。

Abstract: Support vector machine (SVM) is one of the best theoretical algorithm to solve the problem of small sample classification. Kernel parameter selection is very important, which directly affects the accuracy of fault diagnosis. In this paper, the particle swarm optimization (PSO) was used to optimize the parameters of SVM, the PSO-SVM model was proposed which was applied to fault diagnosis of track circuit. By comparing the MATLAB simulation results, it was concluded that the parameters obtained by PSO were better than the random parameters, and the fault diagnosis accuracy of the established PSO-SVM model was higher than that of the ordinary SVM model.