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ID:62062905
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时间:2021-04-14
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1、最新6MLP神经网络-药学医学精品资料contentsstructureuniversaltheoremMLPforclassificationmechanismofMLPforclassificationnonlinearmappingbinarycodingoftheareasMLPforregressionlearningalgorithmoftheMLPbackpropagationlearningalgorithmheuristicsinlearningprocessXORandLinearSeparabilityRevis
2、itedRememberthatitisnotpossibletofindweightsthatenableSingleLayerPerceptronstodealwithnon-linearlyseparableproblemslikeXOR:However,Multi-LayerPerceptrons(MLPs)areabletocopewithnon-linearlyseparableproblems.Historically,theproblemwasthattherewerenolearningalgorithmsfortr
3、ainingMLPs.Actually,itisnowquitestraightforward.ExpressivepowerofanMLPQuestionsHowmanyhiddenlayersareneeded?Howmanyunitsshouldbeina(the)hiddenlayer?AnswersKomogorov’smappingneuralnetworkexistencetheorem(universaltheorem)Komogorov’smappingneuralnetworkexistencetheorem(un
4、iversaltheorem)Anycontinuousfunctiong(x)definedontheunithypercubecanberepresentedinthefromForproperlychosenfunctionsandItisimpracticalthefunctionsandarenotthesimpleweightedsumspassedthroughnonlinearitiesfavoredinneuralnetworksIttellsusverylittleabouthowtofindthenonlinea
5、rfunctionsbasedondata—thecentralprobleminnetworkbasedpatternrecognitionthosefunctionscanbeextremelycomplex;theyarenotsmoothKomogorov’smappingneuralnetworkexistencetheorem(universaltheorem)Anycontinuousfunctiong(x)canbeapproximatedtoarbitraryprecisionbyforproperlychosenf
6、unctionf(.)whenNHapproachestoinfinity.MLPforclassificationMLPforregressionLearningschemeSupervisedlearningTwopropagationdirections-FunctionSignal:inforwarddirection-Errorsignal:inbackwarddirectionLearninginMLPObjectivefunctionwhereThedesiredoutputofthejthoutputneuronThe
7、realoutputofthejthoutputneuronSumsquarederrorfunctionSteepestdescentsearchmethodPartialderivativeextensionLearningrateparameterSynapticweightfromithneuronink-1thlayertothejthneuroninkthlayerofthenetworkSituationforSituationforBackpropagationlearningalgorithmofMLPUpdatin
8、gequationwherewhichisBackpropagationformulaForsigmoidalfunctionf(.)wehaveSpeedingthelearningprocessLearningrat
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