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ID:36503981
大小:2.04 MB
页数:48页
时间:2019-05-11
《基于神经网络的商场客流量统计系统研究》由会员上传分享,免费在线阅读,更多相关内容在学术论文-天天文库。
1、河北工业大学硕士学位论文基于神经网络的商场客流量统计系统研究姓名:周瑞英申请学位级别:硕士专业:模式识别与智能系统指导教师:顾军华20061101基于神经网络的商场客流量统计系统研究STUDYOFPEOPLE-COUNTINGSYSTEMINMARKETPLACEBASED-ONNEURALNETWORKABSTRACTAstheentryofmanyforeignretailenterprises,thecompetitionisfiercetoretaildaybyday.Customer-countingisveryimportant,becauseitisafoundationo
2、fretailinganditisproportionaltosalesamount.Thetraditionalcountingmethodartificiallycan’tofferthereal-timeflowofcustomerdata,whiletherateofaccuracy,whichofsimplecountingwithphotoelectrictransducerofinfraredray,islow.Soitisanimportantroutetoimprovetherateofaccuracyofinfrared-countingwithpatternreco
3、gnitiontechnologyofartificialintelligence.Thispaperdesignsanewpeople-countingsystembased-onneuralnetwork,AccordingtotheresearchapproachofpatternrecognitionsystemandtheCharacteristicofthedata,whichphotoelectrictransducerofinfraredraygets.Thissystemincludesfourphotoelectrictransducersofinfraredray,
4、whicharefixedintheentryofMarket,andtheheighttotheankle.Whenthecustomeristhroughcountingthearea,itwillproduceapattern.Andthepatternisdealtwithbytheneuralnetworkofintelligence.Itcountsthenumberofcustomersandsavesit.Thegroundworkisasfollows:Thispaperchoosesfourphotoelectrictransducersofinfraredray,i
5、nordertodistinguishcustomerswhichenteratthesametime.Andtheinitialdataispreprocessedforstrengtheningthevalidityofdata.Dataofcustomersiscontinuousspace-timesequence.Accordingtothischaracteristic,thispaperhasproposedanadaptedmethodofdatasegmentation.Andtheexperimentprovesthiskindofmethodiseffective.
6、Thispaperhasproposedanextractionmethodoffeatureparameterbasedonpulse-pulsesequence.Andtheexperimentindicatesthiskindofmethodiseffective.ThispaperchoosesresilientBlack-Propagations.Anditlearnsalotofsamplesandusedtocountthenumberofcustoms.Theexperimentindicatesthissystemisvalid.Itcandistinguishcust
7、omersbetweenwhomtherearesomespaces,andtherateofaccuracyattachesto100.Andforcustomerswhoentryatthesametime,italsohasahighrateofaccuracy.Besides,theexperimentprovesthemodelofcustomer-countingiscorrect.KEYWORDS:customerco
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