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《廣義神經(jīng)網(wǎng)絡(luò)的研究及其在交通流預(yù)測(cè)中的應(yīng)用》由會(huì)員上傳分享，免費(fèi)在線閱讀，更多相關(guān)內(nèi)容在學(xué)術(shù)論文-天天文庫(kù)。
1、大連理工大學(xué)碩士學(xué)位論文廣義神經(jīng)網(wǎng)絡(luò)的研究及其在交通流預(yù)測(cè)中的應(yīng)用姓名：苑文江申請(qǐng)學(xué)位級(jí)別：碩士專業(yè)：計(jì)算機(jī)應(yīng)用技術(shù)指導(dǎo)教師：譚國(guó)真20050316廣義神經(jīng)網(wǎng)絡(luò)的研究及其在交通流預(yù)測(cè)中的應(yīng)用ResearchofGeneralizedNeuralNetworkandItsApplicationinTrafficFlowForecastingAbstractAsanimportantaspectofIntelligemTransportationSystemsors)，trigflowguidanceisconsideredasanopti
2、mumwaytoimprovetrRf并Cefficiencyandmobility．TheessentialoftheTrafficFlowGuidanceSystems(TFGS)aresupplingreal-timeexact蛐cinformation．Trafficflowisimportantinformationinurbantraffic，SOtrafficflowforecastinghasimportantsignificance．Therearemanyfactorsthatcarlinfluencethetraf
3、iCicflow，alloftheseresultsinthedifficultiesofreal-timewafficflowforecasting．Owingtothegoodadaptability，neuralnetworkhasbecomeacommonmodelforinformationforecasting．Basedontraditionalneuralnetwork,thispaperpresentsainteUigentneuronmodel，whichiscomposedoflinearlyindependemf
4、unctionsandSigmoidfunctionwithadjustableparameters．Itisprovedthattheinformationstorageabilityofthisintefiigemneuronisgreatlyimpmvedcomparedwithtraditionalones，consequentlygreatlyimprovestheinformationprocessingabilityofthewholeneuralnetwork．Meanwhile，inordertoreducethesi
5、zeoftheneuralnetwork’Sinput,thispaperUSeSthecorrelationtheorytoanalyzethecorrelationbetweenneighborroadsectiOIlS，andchoosethetra伍cflowofdifferentroadsections，whichhasstrongcorrelationwiththebeingforecastingoneasneuralnetwork’Sinputs，andestablishthetrafficflowmodelbasedon
6、generalizedneuralnetwork．Experimentresultsshowthat,thegeneralizedneuralnetworkconvergesfasterthantraditionalBPneuralnetwork,andmeetpracticalrequirementswell．Inordertogreatlyimprovedtheconvergespeedofgeneralizeneuralnetwork，thispaperdesignsaparalleltrairfingalgorithm，whic
7、hisbasedontrainingsetdecomposition．Thisparalleltrainingalgorithmusesanewcommunicationprofile．Thisprofilegreatlyreducesthecommunicationcostoftheparallelalgorithm．Experimentresultsshowthat，thisparalleltrainingalgorithmiseffectiveforreducingthetrainingtimeofgeneralizedneura
8、lnetwork．KeyWords：IntelligentNeuron；GeneralizedNeuralNetwork；TrafficFlowForecasting；ParallelComputing；G
