基于自适应粒子群优化BP神经网络的高速电主轴温度预测模型（英文）-豆柴文库

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基于自适应粒子群优化BP神经网络的高速电主轴温度预测模型（英文）
Title:AHigh-SpeedElectricMainShaftTemperaturePredictionModelbasedonAdaptiveParticleSwarmOptimizationBPNeuralNetwork
Abstract:
Theaccuratepredictionofhigh-speedelectricmainshafttemperatureiscrucialforthesafeandreliableoperationofmachineryinvariousindustries.Inthispaper,anovelpredictionmodelbasedonahybridapproachofAdaptiveParticleSwarmOptimization(APSO)andBackPropagation(BP)NeuralNetworkisproposed.TheAPSOalgorithmisemployedtooptimizethetrainingprocessoftheBPneuralnetwork,enhancingitsconvergencespeedandaccuracy.Theproposedmodelistestedonreal-worlddatafromahigh-speedelectricmainshaftandcomparedwithtraditionalBPneuralnetworkmodels.TheexperimentalresultsdemonstratethattheproposedAPSO-BPneuralnetworkmodeloutperformsthetraditionalBPneuralnetworkmodels,achievingamoreaccurateandefficientforecastofthemainshafttemperature.
1.Introduction
High-speedelectricmainshaftsarewidelyusedinvariousindustries,suchaspowerplants,factories,andtransportationsystems.Thetemperatureofthesemainshaftsisacriticalparameterthatdirectlyaffectsthesafeoperationandperformanceofthemachinery.Accuratetemperaturepredictionhelpsinmonitoringandcontrollingthemainshafttemperature,preventingequipmentfailure,andensuringoperationalefficiency.Therefore,thedevelopmentofareliableandefficientpredictionmodelformainshafttemperatureisofgreatsignificance.
2.RelatedWork
Variouspredictionmodelshavebeenproposedfortemperaturepredictionindifferentfields,suchasartificialintelligence-basedmodels,statisticalmodels,andmachinelearningmodels.Amongthesemodels,BPneuralnetworkshaveshownpromisingresultsintimeseriesforecastingduetotheirabilitytocapturenon-linearrelationshipsandadapttocomplexsystems.However,thetrainingprocessofBPneuralnetworksisoftentime-consuming,andtheconvergenceratecouldbeslow.
3.Methodology
Inthispaper,weproposeahybridmodelthatcombinesAPSOalgorithmandBPneuralnetworktoaddressthelimitationsoftraditionaltrainingalgorithms.TheAPSOalgorithmisavariantofthetraditionalParticleSwarmOptimization(P