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基于反向传播的电路优化与模型参数提取方法.docx

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基于反向传播的电路优化与模型参数提取方法 Title:CircuitOptimizationandParameterExtractionMethodBasedonBackpropagation Abstract: Theabilitytooptimizeandaccuratelyextractmodelparametersiscriticalforimprovingtheefficiencyandperformanceofcircuits.Inthispaper,weproposeamethodbasedonthebackpropagationalgorithmforcircuitoptimizationandmodelparameterextraction.Backpropagationisawidelyusedalgorithminmachinelearningforupdatingtheweightsandbiasesofneuralnetworks.Weadaptthisalgorithmtooptimizecircuitarchitecturesandaccuratelyextractmodelparameters.Ourmethodaimstoimprovecircuitefficiency,reducepowerconsumption,andenhanceoverallperformance.Theeffectivenessoftheproposedmethodisdemonstratedthroughexperimentsandcomparisonswithexistingtechniques. 1.Introduction: Circuitoptimizationandmodelparameterextractionplayavitalroleinenhancingtheefficiencyandeffectivenessofcircuits.Traditionally,circuitdesignershavereliedonmanualmethodstooptimizecircuitarchitecturesandextractmodelparameters.However,thesemanualapproachesaretime-consuming,error-prone,anddonotguaranteeoptimalsolutions.Therefore,thereisaneedforautomatedmethodsthatcanoptimizecircuitsandextractaccuratemodelparameters.Inthispaper,weproposeanovelmethodbasedonbackpropagation,apowerfulalgorithminmachinelearning,toaddressthesechallenges. 2.Background: 2.1BackpropagationAlgorithm: Thebackpropagationalgorithmiswidelyusedinneuralnetworksforupdatingtheweightsandbiasesofnodes.Itinvolvescalculatingthegradientofthelossfunctionwithrespecttotheweightsandbiasesandthenadjustingthemaccordingly.Thealgorithmisbasedonthechainruleandallowsforefficientlearninginmultilayerneuralnetworks.Weadaptthisalgorithmtooptimizecircuitarchitecturesandefficientlyextractmodelparameters,leveragingitsabilitytolearnfrominput-outputrelationships. 2.2CircuitOptimization: Circuitoptimizationinvolvesimprovingtheefficiency,reducingpowerconsumption,andenhancingtheperformanceofcircuits.Ourproposedapproachutilizesthebackpropagationalgorithmtooptimizecircuitarchitecturesbyupdatingtheweightsandbiasesofc