基于正则化的函数连接神经网络研究及其复杂化工过程建模应用-豆柴文库

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基于正则化的函数连接神经网络研究及其复杂化工过程建模应用
Abstract
Function-connectionneuralnetwork(FCNN)isapowerfultoolformodelingcomplexsystemsandpredictingtheirbehaviors.Inthispaper,weinvestigatetheuseofFCNNwithregularizationformodelingcomplexchemicalprocesses.WedemonstratehowFCNNimprovesupontraditionalmethodsofprocessmodeling,suchaslinearregressionandpartialleastsquaresregression.WepresentacasestudyofapplyingFCNNtomodelacomplexchemicalprocessinthepharmaceuticalindustry.TheresultsshowthatFCNNwithregularizationoutperformsothermodelingmethodsinaccuracyandreliability.
Introduction
Modelingcomplexchemicalprocessesisacrucialtaskformanyindustries,suchaspharmaceuticals,polymers,andchemicals.Accuratelypredictingprocessbehaviorscanleadtosignificantcostsavingsandimprovedefficiency.Traditionalmethodsofprocessmodelingincludelinearregression,partialleastsquaresregression,andprincipalcomponentregression.Althoughthesemethodshavebeenwidelyapplied,theyhavecertainlimitationssuchasbeingrestrictedtolinearrelationshipsandbeingsensitivetooutliers.
Function-connectionneuralnetwork(FCNN),alsoknownasradialbasisfunction(RBF)network,isatypeofneuralnetworkthatcanmodelbothlinearandnonlinearrelationships.FCNNconsistsofthreelayers:input,hidden,andoutput.Thehiddenlayerconsistsofradialbasisfunctions,whichareusedtotransformtheinputdataintoahigherdimensionalspace.Theoutputlayerisalinearcombinationofthehiddenlayeroutputs.Theweightsandbiasesofthenetworkaredeterminedusingatrainingalgorithm,suchasthebackpropagationalgorithm.
However,FCNNcansufferfromoverfitting,whichoccurswhenthemodelistoocomplexandfitsthetrainingdatatoowell,resultinginpoorgeneralizationtonewdata.Toovercomethisproblem,regularizationtechniquescanbeappliedtothenetwork.Regularizationaddsapenaltytermtothelossfunction,whichencouragesthemodeltohavesimplerweightsandavoidsoverfitting.TwocommontypesofregularizationtechniquesareL1regularization,whichencouragessparseweights,andL2regularization,whichencouragessmallweights.
Inthispaper,weinvestigatetheuseofFCNNwithregularizat