摘要
针对在柴油机故障诊断中支持向量机核函数参数和惩罚因子的不同取值会影响到分类正确率的问题,提出利用和声搜索算法对支持向量机相关参数进行选择优化(HS-SVM)的方法。在该方法中将参数作为和声存于记忆库中,而将支持向量机分类正确率作为目标函数,则整个寻优过程即是寻找使函数值最大的和声所对应的解。通过对UCI中的2个数据集进行分类正确率测试,并与未优化的支持向量机和人工蜂群算法优化的支持向量机(ABC-SVM)测试结果对比,证明了该方法的优越性。最后,将该方法用于柴油机故障诊断,并将分类正确率与未优化SVM和ABC-SVM分类结果进行比较,进一步说明和声搜索算法优化的支持向量机(HS-SVM)既能获得较高的分类结果正确率,又能有效降低运行时间,即说明该方法具有一定的实用性。
In view of the problem which different values of Support vector machine kernel function parame-ters and penalty factor will affect the classification accuracy in diesel engine fault diagnosis, this paper uses harmony search algorithm to optimize parameters of support vector machine（ HS-SVM） . In the method, the parameters will be stored in memory as a backup, and support vector machine classification accuracy as the objective function, so the optimization process is to look for the solution of harmony with the biggest func-tion value. Through classification accuracy test of two UCI data sets and comparison with test results from unoptimized SVM and ABC-SVM, it demonstrates the superiority of the method. Finally, the method is for diesel engine fault diagnosis, and through comparison with unoptimized SVM and ABC-SVM, it further proves that HS-SVM can obtain higher classification accuracy, and reduce the running time effectively. Namely, it illustrates practicability of the proposed method.
出处
《组合机床与自动化加工技术》
北大核心
2015年第9期66-70,共5页
Modular Machine Tool & Automatic Manufacturing Technique
基金
国家自然科学基金资助项目(51175480)
关键词
和声搜索算法
支持向量机
参数优化
柴油机故障诊断
harmony search algorithm
support vector machine
parameter optimization
diesel engine fault diagnosis
