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1、基于遗传算法优化BP神经网络的接触电阻预测孙海峰,沈颖,王亚楠(华北电力大学电气与电子工程学院,河北保定071003)摘要:接触电阻是反应导体间电接触性能的重要参数,在实际的工程中往往采用经验公式对接触电阻进行计算,精度难以满足要求。为解决这一问题,将遗传算法(GA)与BP神经网络相结合对接触电阻进行预测。通过实验得到数据,分别利用遗传算法优化BP神经网络、BP神经网络以及回归分析模型进行训练和测试,并将各算法所得误差进行对比。误差对比结果表明:遗传算法优化BP神经网络的收敛效果优于其他两种算法,且
2、遗传算法优化BP神经网络所得接触电阻模型的相对误差平均值比BP神经网络减少了4.01%,比回归分析减少了4.72%,且预测效果较稳定。利用遗传算法与BP神经网络相结合的接触电阻预测模型较单独使用BP神经网络预测模型具有更好的非线性拟合能力和更高的预测精度。关键词:电接触;接触电阻;遗传算法;BP神经网络;回归分析中图分类号:TM769文献标识码:A文章编号:1001-1390(2019)00-0000-00PredictionofcontactresistancebasedonoptimizedBP
3、neuralnetworkofgeneticalgorithmtooptimizeBPneuralnetworkSunHaifeng,ShenYing,WangYanan((SchoolofElectricalandElectronicalElectronicEngineering,NorthChinaElectricPowerUniversity,Baoding071003,Hebei,China))Abstract:Thecontactresistanceisanimportantparamet
4、eroftheelectricalcontactperformancebetweenthereactionconductors.Inpractice,theempiricalformulaisoftenusedtocalculatethecontactresistance,whichisdifficulttomeettheaccuracyrequirements.InorderTotosolvethisproblem,thegeneticalgorithm(GA)combinedwithBPneur
5、alnetworkisadoptedtopredictthecontactresistance.ThedataareobtainedThroughthroughexperiment,,thedataareobtained,andtheoptimizedBPneuralnetworkofgeneticalgorithmoptimizedBPneuralnetwork,theBPneuralnetworkandregressionanalysismodelarerespectivelyusedfortr
6、ainingandtesting,andtheerrorsobtainedbyeachalgorithmarecompared.TheresultsoferrorcomparisonshowthatgeneticalgorithmoptimizestheconvergenceeffectofBPneuralnetworkbetterthantheothertwoalgorithms,andtheaveragerelativeerrorofthecontactresistancemodelobtain
7、edbygeneticalgorithmoptimizationBPneuralnetworkisreducedby4.01%comparedwithBPneuralnetwork,whichislowerthantheregressionanalysis4.72%,andtheforecastingeffectismorestable.ThecontactresistancepredictionmodelusinggeneticalgorithmandBPneuralnetworkhasbette
8、rnonlinearfittingabilityandhigherpredictionaccuracythantheBPneuralnetworkpredictionmodelalone.Keywords:electricalcontact,contactresistance,geneticalgorithm,BPneuralnetwork,regressionanalysis0引言在电力系统、自动控制系统和信息传递系统等领域广泛存在着电接触,如阀厅金具的连接处、继电