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1、NeuralNetworkDesignHaganDemuth2ndEditionBealeDeJesúsNeuralNetworkDesign2ndEdtionMartinT.HaganOklahomaStateUniversityStillwater,OklahomaHowardB.DemuthUniversityofColoradoBoulder,ColoradoMarkHudsonBealeMHBInc.Hayden,IdahoOrlandoDeJesúsConsultantFrisco,TexasCopy
2、rightbyMartinT.HaganandHowardB.Demuth.Allrightsreserved.Nopartofthebookmaybereproduced,storedinaretrievalsystem,ortranscribedinanyformorbyanymeans-electronic,mechanical,photocopying,recordingorotherwise-withoutthepriorpermissionofHaganandDemuth.MTHToJanet,Tho
3、mas,Daniel,MomandDadHBDToHal,Katherine,KimberlyandMaryMHBToLeah,Valerie,Asia,Drake,CoralandMorganODJTo:Marisela,MaríaVictoria,Manuel,MamáyPapá.NeuralNetworkDesign,2ndEdition,eBookOVERHEADSandDEMONSTRATIONPROGRAMScanbefoundatthefollowingwebsite:hagan.okstate.e
4、du/nnd.htmlAsomewhatcondensedpaperbackversionofthistextcanbeorderedfromAmazon.ContentsPrefaceIntroductionObjectives1-1History1-2Applications1-5BiologicalInspiration1-8FurtherReading1-102NeuronModelandNetworkArchitecturesObjectives2-1TheoryandExamples2-2Notati
5、on2-2NeuronModel2-2Single-InputNeuron2-2TransferFunctions2-3Multiple-InputNeuron2-7NetworkArchitectures2-9ALayerofNeurons2-9MultipleLayersofNeurons2-10RecurrentNetworks2-13SummaryofResults2-16SolvedProblems2-20Epilogue2-22Exercises2-23iAnIllustrativeExample3O
6、bjectives3-1TheoryandExamples3-2ProblemStatement3-2Perceptron3-3Two-InputCase3-4PatternRecognitionExample3-5HammingNetwork3-8FeedforwardLayer3-8RecurrentLayer3-9HopfieldNetwork3-12Epilogue3-15Exercises3-164PerceptronLearningRuleObjectives4-1TheoryandExamples4
7、-2LearningRules4-2PerceptronArchitecture4-3Single-NeuronPerceptron4-5Multiple-NeuronPerceptron4-8PerceptronLearningRule4-8TestProblem4-9ConstructingLearningRules4-10UnifiedLearningRule4-12TrainingMultiple-NeuronPerceptrons4-13ProofofConvergence4-15Notation4-1
8、5Proof4-16Limitations4-18SummaryofResults4-20SolvedProblems4-21Epilogue4-33FurtherReading4-34Exercises4-36iiSignalandWeightVectorSpaces5Objectives5-1TheoryandExamples5-2LinearVectorSpaces
