基于最小二乘支持向量机的黏着状态辨识

针对重载机车黏着状态辨识中分类准确率不高的问题,提出采用布谷鸟遗传算法对最小二乘支持向量机的参数进行优化,并采用交叉验证原理提高该模型的整体泛化性能。首先,采用布谷鸟算法寻找惩罚因子和核参数的初始值;然后,采用遗传算法对最小二乘支持向量机进行训练,从而得到具有最佳参数的最小二乘支持向量机的分类模型。该分类模型将重载机车黏着状态分为正常、故障征兆、微小故障和严重故障4个状态。实验结果表明,提出的最小二乘支持向量机模型在黏着状态辨识中的分类准确率高达94.59%,高于极限学习机的分类准确率(84.61%),证明布谷鸟遗传算法能够有效提高最小二乘支持向量机的分类准确率。

Adhesion State Identification Based on Least Square Support Vector Machines

In view of the low classification accuracy in the identification of heavy duty locomotive adhesion state, a genetic algorithm based on cuckoo has been proposed to optimize the parameters of least squares support vector machines, with the cross validation method adopted to improve the overall generalization performance of the model. First, the cuckoo algorithm is used to find the initial values of penalty parameters and kernel functions. Next, the genetic algorithm is used to train the least squares support vector machines (SVM), thus obtaining the best parameters of the least squares support vector machines (SVM) model. Under this classification model, the adhesion states of heavy duty locomotive can be divided into four categories: normal condition, fault symptom state, minor fault state and serious fault state. Experimental results show that the classification accuracy of the proposed least squares support vector machine model can reach as high as 94.59%, much higher than that of the limit learning machines with its classification accuracy only being 84.61%. Therefore it is proved that the genetic algorithm can effectively improve the classification accuracy of the least squares support vector machines.