简介：在这个工作，有为BP神经网络的动量的一个坡度方法被考虑。动量系数以一种适应方式被选择加速并且稳定网络重量的学习过程。相应集中结果被证明。

简介：Anewsystemisdevelopedtorecognizepromotersequencesfromnon-promotersequencesbasedonpositionweightmatrixandbackpropagationneuralnetworkinthispaper.Thesystemperformssignificantlybetteronthetrainingsetandthetestset,themeanrecognitionrateisashighas99%onthetrainingsetand97%onthetestingset.Experimentalresultsdemonstratetheeffectivenessofthesystemtorecognizethepromotersequencesthathavebeentrainedandthepromotersequencesthathavenotbeenseenpreviously.

简介：100piecesof26650-typeLithiumironphosphate（LiFePO4）batteriescycledwithafixedchargeanddischargeratearetested,andtheinfluenceofthebatteryinternalresistanceandtheinstantaneousvoltagedropatthestartofdischargeonthestateofhealth（SOH）isdiscussed.Abackpropagation（BP）neuralnetworkmodelusingadditionalmomentumisbuiltuptoestimatethestateofhealthofLi-ionbatteries.Theadditional10piecesareusedtoverifythefeasibilityoftheproposedmethod.Theresultsshowthattheneuralnetworkpredictionmodelhaveahigheraccuracyandcanbeembeddedintobatterymanagementsystem（BMS）toestimateSOHofLiFePO4Li-ionbatteries.

简介：MostofthestudiesonArtificialNeuralNetwork(ANN)modelsremainrestrictedtosmallerriversandcatchments.Inthispaper,anattempthasbeenmadetocorrelatevariabilityofsedimentloadswithrainfallandrunoffthroughtheapplicationoftheBackPropagationNeuralNetwork(BPNN)algorithmforalargetropicalriver.ThealgorithmandsimulationaredonethroughMATLABenvironment.Themethodologycomprisedofacollectionofdataonrainfall,waterdischarge,andsedimentdischargefortheNarmadaRiveratvariouslocations(alongwithtimevariables)andapplicationtodevelopathreelayerBPNNmodelforthepredictionofsedimentdischarges.Fortrainingandvalidationpurposesasetof549datapointsforthemonsoon(16June-15November)periodofthreeconsecutiveyears(1996–1998)wasused.Fortestingpurposes,theBPNNmodelwasfurthertrainedusingasetof732datapointsofmonsoonseasonoffouryears(2006–07to2009–10)atninestations.Themodelwastestedbypredictingdailysedimentloadforthemonsoonseasonoftheyear2010–11.ToevaluatetheperformanceoftheBPNNmodel,errorswerecalculatedbycomparingtheactualandpredictedloads.Thevalidationandtestingresultsobtainedatalltheselocationsaretabulatedanddiscussed.Resultsobtainedfromthemodelapplicationarerobustandencouragingnotonlyforthesub-basinsbutalsofortheentirebasin.Theseresultssuggestthattheproposedmodeliscapableofpredictingthedailysedimentloadevenatdownstreamlocations,whichshownonlinearityinthetransportationprocess.Overall,theproposedmodelwithfurthertrainingmightbeusefulinthepredictionofsedimentdischargesforlargeriverbasins.

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