化工自动化及仪表
化工自動化及儀錶
화공자동화급의표
Control and Instruments in Chemical Industry
2015年
11期
1237-1241,1249
,共6页
支持向量机%参数优化%和声搜索算法%故障诊断
支持嚮量機%參數優化%和聲搜索算法%故障診斷
지지향량궤%삼수우화%화성수색산법%고장진단
SVM%parameter optimization%harmony search algorithm%fault diagnosis
提出了一种改进和声搜索算法以优化支持向量分类机的参数，该方法可以随着迭代次数的增加自适应动态调整参数。在核电站电动隔离阀门故障分类预测的仿真实验中，将该方法与经典智能算法优化参数的支持向量分类机对比，基于改进和声搜索算法的支持向量分类机具有更高的分类精度和泛化能力。
提齣瞭一種改進和聲搜索算法以優化支持嚮量分類機的參數，該方法可以隨著迭代次數的增加自適應動態調整參數。在覈電站電動隔離閥門故障分類預測的倣真實驗中，將該方法與經典智能算法優化參數的支持嚮量分類機對比，基于改進和聲搜索算法的支持嚮量分類機具有更高的分類精度和汎化能力。
제출료일충개진화성수색산법이우화지지향량분류궤적삼수，해방법가이수착질대차수적증가자괄응동태조정삼수。재핵전참전동격리벌문고장분류예측적방진실험중，장해방법여경전지능산법우화삼수적지지향량분류궤대비，기우개진화성수색산법적지지향량분류궤구유경고적분류정도화범화능력。
The method of improving harmony search algorithm to optimize the parameters of support vector classification machine was proposed.This method can respond to the dynamic-regulation parameters with the increase of iterations.In simulation experiment of the dynamoelectric isolation valve’ s fault classification and prediction in the nuclear power plant, having this method compared with the classical parameter optimization algorithm shows that this support vector machine based on the improved harmony search algorithm has better classification accuracy and generalization ability.
