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外文资料--Broiler Growth Performance Analysis from.PDF

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外文资料--Broiler Growth Performance Analysis from.PDF

BROILERGROWTHPERFORMANCEANALYSISFROMCORRELATIONANALYSIS,MULTIPLELINEARREGRESSION,TONEURALNETWORKMEIYANXIAO,PEIJIEHUANG,PIYUANLIN,SHANGWEIYANCOLLEGEOFINFORMATICSSOUTHCHINAAGRICULTURALUNIVERSITYGUANGZHOU,CHINACORRESPONDINGAUTHORPJHUANGSCAUEDUCNABSTRACTTHEPURPOSEOFTHISSTUDYISTOINVESTIGATETHEDATAWEUSETHEBROILERGROWTHDATASETOFTHEMOSTFAMOUSFITTINGFORBROILERGROWTHPERFORMANCEPARAMETERSINTHISPAPER,POULTRYRAISINGCOMPANYINCHINATOEVALUATEOURAPPROACHANDTHEGRADUALADVANCINGANALYSISMETHODS,FROMCORRELATIONTHERESULTSSHOWTHEEFFECTIVENESSOFOURAPPROACHANALYSIS,MULTIPLELINEARREGRESSION,TONEURALNETWORK,AREPROPOSEDTHEMEANTECHNOLOGYROADMAPISFIRSTLY,CORRELATIONTHERESTOFTHISPAPERISORGANIZEDASFOLLOWSINTHENEXTANALYSISISUSEDTODETECTTHEDEGREEOFCORRELATIONBETWEENTHESECTION,WEPRESENTTHEGRADUALADVANCINGANALYSISMETHODSBROILERGROWTHPERFORMANCEPARAMETERANDTHECANDIDATEINPUTEXPERIMENTSAREPRESENTEDANDDISCUSSEDINSECTION3FINALLY,VARIABLESANDTHENCHOOSETHEPREDICTORVARIABLESTHATHAVEGOODSECTION4LISTSSOMECONCLUSIONSCORRELATIONWITHTHEDEPENDENTVARIABLETOBUILDTHEMULTIPLELINEARREGRESSIONORNEURALNETWORKPREDICTIONMODEL,ORBOTH,IIGRADUALADVANCINGANALYSISMETHODSACCORDINGTOTHELINEARDEGREEOFCORRELATIONSCOMBINEDPREDICTIONMAYBECHOSEONCEBOTHMODELSHAVEGOODPREDICTIONTHEEXPLOREMETHODSINOURSTUDYOFDATAFITTINGFORBROILERPERFORMANCESWEUSETHEBROILERGROWTHDATASETOFTHEMOSTGROWTHPERFORMANCEPARAMETERSISDEVELOPEDSTEPBYSTEP,FROMFAMOUSPOULTRYRAISINGCOMPANYINCHINATOEVALUATEOURCORRELATIONANALYSISTOMLR,ANDTHENTONONLINEARFITTINGAPPROACHANDTHERESULTSSHOWTHEEFFECTIVENESSOFOURAPPROACHMEANSBYNEURALNETWORKKEYWORDSGROWTHPERFORMANCE;CORRELATIONANALYSIS;MULTIPLEATECHNOLOGYROADMAPLINEARREGRESSION;NEURALNETWORK;BROILERBREEDINGTHETECHNOLOGYROADMAPOFOURGRADUALADVANCINGANALYSISMETHODSISSHOWNINFIG1IINTRODUCTIONBIOINFORMATICS1ISAPROMISINGYOUNGFIELDTHATAPPLIESCOMPUTERTECHNOLOGYINBIOLOGYANDDEVELOPSALGORITHMSANDMETHODSTOMANAGEANDANALYZEBIOLOGICALDATA2FORTHEMODERNPOULTRYBREEDINGCOMPANIES,ITISDESERVEDTOPREDICTTHEPOULTRYGROWTHPERFORMANCEPARAMETERS,SUCHASRATEFORSALE,FEEDINTAKE,DAILYGAINANDFEEDCONVERSIONRATIO,BASEDONTHEMASSIVEHISTORICALDATAGRADUALLYCUMULATEDINPRODUCTIONHOWEVER,BECAUSEOFTHECOMPLEXITYANDUNCERTAINTYBRINGBYTHEINFLUENCEOFENVIRONMENTALANDPHYSIOLOGICALFACTORS,INFORMATIONINTEGRATIONOFBIOLOGICALDATAISACHALLENGEINTHISPAPER,THEGRADUALADVANCINGANALYSISMETHODS,FROMCORRELATIONANALYSIS,MULTIPLELINEARREGRESSIONMLR3,TONEURALNETWORK4,AREPROPOSEDTOSTUDYTHEDATAFITTINGFORBROILERGROWTHPERFORMANCEPARAMETERSINBROILERBREEDING,SEASONALFACTORPLAYSANIMPORTANTPARTONTHEEFFECTOFSEASONALFACTORS,BROILERGROWTHPERFORMANCEFIGURE1TECHNOLOGYROADMAPOFTHEPROPOSEDMETHODSCANBEOBVIOUSLYDIFFERENTSOBROILERGROWTHPERFORMANCETHEASSOCIATIONBETWEENVARIABLESCANBELINEARORPARAMETERSHAVEOBVIOUSSEASONALVARIATIONSEASONALFACTORSNONLINEARCORRELATIONANALYSISISMOSTLYUSEDTOEVALUATELINEARINCLUDEAIRTEMPERATURE,PRECIPITATION,WINDSPEED,PRESSURE,RELATIONSHIPSASSOCIATIONSBETWEENTWOVARIABLESCANBERELATIVEHUMIDITY,ETCTHISPAPERTAKESTHEINFLUENCEOFTHEAIRANALYZEDWITHABIVARIATECORRELATIONANALYSISWHILETEMPERATURETOTHERATEFORSALEFOREXAMPLETOINTRODUCETHEASSOCIATIONSBETWEENONEDEPENDENTVARIABLEANDASETOFTWOBROILERGROWTHPERFORMANCEANALYSISMETHODSORMOREINDEPENDENTVARIABLES,WHICHHAVESTRONGTHISWORKISSUPPORTEDBYTHESCITECHRESEARCHPROJECTOFGUANGDONGPROVINCEUNDERGRANTNO2007A020300010,THENATIONAL863HIGHTECHRESEARCHDEVELOPMENTPLANOFCHINAUNDERGRANTNO2006AA10Z246,ANDTHENEWDISCIPLINESUPPORTINGFUNDOFSOUTHCHINAAGRICULTURALUNIVERSITYUNDERGRANTNO2007X022NOTLINEARENOUGHDEPENDENTVARIABLEINDEPENDENTVARIABLESCORRELATIONANALYSISCOMPARISONCOMBINEDPREDICTIONWHENBOTHHAVEGOODPREDICTRESPONSESMULTIPLELINEARREGRESSIONNEURALNETWORKSTRONGLINERCORRELATION9781424447138/10/25002010IEEECORRELATIONSWITHTHEDEPENDENTVARIABLE,CANBESTUDIEDUSINGMULTIPLECORRELATIONREGRESSIONANALYSIS,SUCHASMLRALTERNATIVELY,IFTHEDEGREEOFCORRELATIONSISNOTLINEARENOUGHBETWEENTHEDEPENDENTVARIABLEANDTHEINDEPENDENTVARIABLES,SOMENONLINEARFITTINGSPROVIDEGOODCHOOSEINTHENONLINEARFITTINGMETHODS,COMPARINGTOGOMPERTZTHATUSINGLEASTSQUARESINNONLINEARREGRESSION,NEURALNETWORKISPROVEDTOHASGOODABILITYTOPREDICTRESPONSES5FINALLY,INPRACTICALAPPLICATION,IFBOTHMLRANDNEURALNETWORKHAVEGOODPREDICTIONPERFORMANCES,WECANCONSIDERTHECOMBINEDPREDICTIONBCORRELATIONANALYSISACORRELATIONANALYSISISASTATISTICALPROCEDURETHATEVALUATESTHEASSOCIATIONBETWEENTHEDEPENDENTVARIABLEANDTHEINDEPENDENTVARIABLESRESPECTIVELYTHESIMPLESTWAYTOFINDOUTQUALITATIVELYTHECORRELATIONISTOPLOTTHEDATAANDWECANQUANTIFYTHEDEGREEOFCORRELATIONBYSPECIFYINGTHECORRELATIONCOEFFICIENTR,DEFINEDASYYINIXXIYXNRΣΣ−−−∑1111WHEREXANDXΣDENOTETHESAMPLEMEANANDTHESAMPLESTANDARDDEVIATIONRESPECTIVELYFORTHEVARIABLEXANDYANDYΣDENOTETHESAMPLEMEANANDTHESAMPLESTANDARDDEVIATIONRESPECTIVELYFORTHEVARIABLEYASSUMETHATAPERFECTLINEARRELATIONSHIPEXISTSBETWEENTHEVARIABLESXANDY,IE,BAXYIIFORI1,2,,NWITH0≠ANOWVERIFYUSINGTHEDEFINITIONSOFTHEMEANANDTHEVARIANCETHATBAXYANDXYAΣΣTHISIMPLIESFROM1THATRA/|A|ORINOTHERWORDS,R1IFA0ANDR1IFA0THECASER1CORRESPONDSTOTHEMAXIMUMPOSSIBLELINEARPOSITIVEASSOCIATIONBETWEENXANDY,MEANINGTHATALLTHEDATAPOINTSWILLLIEEXACTLYONASTRAIGHTLINEOFPOSITIVESLOPESIMILARLY,R1CORRESPONDSTOTHEMAXIMUMPOSSIBLENEGATIVEASSOCIATIONBETWEENTHESTATISTICALVARIABLESXANDYINGENERAL,1≤R≤1WITHTHEMAGNITUDEANDTHESIGNOFRREPRESENTINGTHESTRENGTHANDDIRECTIONRESPECTIVELYOFTHEASSOCIATIONBETWEENTHETWOVARIABLESCMULTIPLELINEARREGRESSIONONCEWEHAVEESTABLISHEDTHATASTRONGCORRELATIONEXISTSBETWEENTHEDEPENDENTVARIABLEANDMORETHANONEINDEPENDENTVARIABLE,WEWILLUSEMLRALINEARREGRESSIONMODELTHATCONTAINSMORETHANONEPREDICTORVARIABLEISCALLEDAMLRMODELTHEFOLLOWINGMODELISAMLRMODELWITHTWOPREDICTORVARIABLES,1XAND2XUXXY2210ΒΒΒ2THEMODELISLINEARBECAUSEITISLINEARINTHEPARAMETERS,0Β,1ΒAND2ΒTHEMODELDESCRIBESAPLANEINTHETHREEDIMENSIONALSPACEOFY,1XAND2XTHEPARAMETER0ΒISTHEINTERCEPTOFTHISPLANEPARAMETERS1ΒAND2ΒAREREFERREDTOASPARTIALREGRESSIONCOEFFICIENTSPARAMETER1ΒREPRESENTSTHECHANGEINTHEMEANRESPONSECORRESPONDINGTOAUNITCHANGEIN1XWHEN2XISHELDCONSTANTPARAMETER2ΒREPRESENTSTHECHANGEINTHEMEANRESPONSECORRESPONDINGTOAUNITCHANGEIN2XWHEN1XISHELDCONSTANTUISTHERANDOMERRORDNEURALNETWORKNEURALNETWORK4OFFERANALTERNATIVETOREGRESSIONANALYSISFORBIOLOGICALMODELINGINRELATIONTOSYSTEMMODELING,THEDIFFERENCEBETWEENARTIFICIALNEURALNETWORKSANDREGRESSIONANALYSISISTHATANEQUATIONISNOTASSUMED,TIGHTERFITSOFDATAAREPOSSIBLE,ANDITISPOSSIBLETOWORKWITH“NOISY”DATAVERYLITTLERESEARCHHASBEENCONDUCTEDTOMODELANIMALGROWTHUSINGARTIFICIALNEURALNETWORKS5,6INOURSTUDY,WECHOOSETHEBACKPROPAGATIONBPNEURALNETWORK,WHICHISAFEEDFORWARDMULTILAYERNETWORKBASEDONTHEBACKPROPAGATIONALGORITHMDEVELOPEDBYRUMELHARTANDMCCELLAND7ANDHASBECOMEONEOFTHEMOSTWIDELYUSEDNEURALNETWORKINPRACTICETHEACTIVATIONTRANSFERFUNCTIONATFOFABPNETWORK,USUALLY,ISADIFFERENTIABLESIGMOIDSSHAPEFUNCTION,WHICHHELPSTOAPPLYNONLINEARMAPPINGFROMINPUTSTOOUTPUTSATWOLAYERBPNETWORKWASUSEDINOURMODELTHEGOODNESSOFFITSFORTHEOBTAINEDNEURALNETWORKMODELWASCALCULATEDBYMEANSQUAREERRORMSEANDMEANPERCENTAGEERRORMPETHEMPEANDMSEARECOMPUTEDAS∑−NTTTTYYYNMPE1ˆ13NYYMSENTTT∑−12ˆ4WHERETYEQUALSTHEOBSERVEDVALUEATTIMET,TYˆEQUALSTHEESTIMATEDVALUE,ANDNEQUALSTHENUMBEROFOBSERVATIONSIIIEXPERIMENTALRESULTSAEXPERIMENTSETUPWETAKETHEBREEDINGAREAOFGUANGDONGPROVINCEOFCHINAFOREXAMPLETOEVALUATEOURAPPROACHTHEDATASETOFTENDAYMEANAIRTEMPERATUREISPROVIDEDBYGUANGDONGPROVINCIALCLIMATEANDAGROMETEOROLOGICALCENTEANDTHEBROILERGROWTHDATASETISPROVIDEDBYGUANGDONGWENSFOODGROUPLIMITEDCOMPANY,WHICHISTHEMOSTFAMOUSPOULTRYRAISINGCOMPANYINCHINAANDWETAKEHENOFSHORTFEETBUFFBFOREXAMPLETOEVALUATETHEINFLUENCEOFTHEAIRTEMPERATURETOTHERATEFORSALEWESELECTHENGROWTHDATAOF2007,WHICHCONSISTSOF5714DATA,ANDREMAIN4209DATAAFTERDATAPREPROCESSING,WHICHISTOELIMINATEABNORMALDATA,SUCHASABNORMALRATEFORSALE,NULLDAYAGE,ANDNULLWEIGHTFORTHEMLRANDNEURALNETWORKMODELS,WESELECT70SAMPLESRANDOMLYFORTRAINING,ANDTHERESTFORTESTINGBCORRELATIONANALYSISCONSIDERINGTHATTHEFULLGROWINGSTAGEOFBROILERCANBEDIVIDEDINTOCHICKLINGSTAGETHEFIRST4WEEKSANDADULTCHICKENSTAGEDIFFERENTSTAGESHAVEDIFFERENTPHYSIOLOGICALCHARACTERISTICSO,INOURSTUDY,FIRSTLY,WEUSESCATTERPLOTSTOSHOWTHERELATIONSHIPBETWEENTHERATEFORSALEANDTHETENDAYMEANAIRTEMPERATUREOFHEN,CHICKLINGSTAGE,ANDADULTCHICKENSTAGERESPECTIVELY,WHICHARESHOWNINFIG2TOFIG4ANDTHEN,THEDEGREESOFCORRELATIONSAREQUANTIFIEDBYCORRELATIONCOEFFICIENTR,WHICHISSHOWNINTABLE109209309409509609709871217222732TENDAYMEANAIRTEMPERATURE℃RATEFORSALEFIGURE2TENDAYMEANAIRTEMPERATUREOFHENVSRATEFORSALE09409509609709871217222732TENDAYMEANAIRTEMPERATURE℃RATEFORSALEFIGURE3TENDAYMEANAIRTEMPERATUREOFCHICKLINGSTAGEVSRATEFORSALE09409509609709871217222732TENDAYMEANAIRTEMPERATURE℃RATEFORSALEFIGURE4TENDAYMEANAIRTEMPERATUREOFADULTCHICKENSTAGEVSRATEFORSALETABLEICORRELATIONCOEFFICIENTCASERTENDAYMEANAIRTEMPERATUREOFHENANDRATEFORSALE08506TENDAYMEANAIRTEMPERATUREOFCHICKLINGSTAGEANDRATEFORSALE08932TENDAYMEANAIRTEMPERATUREOFADULTCHICKENSTAGEANDRATEFORSALE08594ASWECANSEEFROMTABLE1,CORRELATIONCOEFFICIENTROFTHERATEFORSALEANDTHETENDAYMEANAIRTEMPERATUREOFCHICKLINGSTAGEANDADULTCHICKENSTAGEISBIGGERTHANTHATOFTHERATEFORSALEANDTHETENDAYMEANAIRTEMPERATUREOFHEN,WHICHINDICATESTHEDIVISIONOFCHICKLINGSTAGEANDADULTCHICKENSTAGETODOFURTHERRESEARCHISARIGHTCHOOSECMULTIPLELINEARREGRESSIONTHEFOLLOWINGMLREQUATIONISFITFORTHETRAININGDATA2105700755036793XXY5WHEREYISTHERATEFORSALE,AND1XAND2XARETHETENDAYMEANAIRTEMPERATUREOFCHICKLINGSTAGEANDADULTCHICKENSTAGERESPECTIVELYDNEURALNETWORKSIMILARTOTHEMLRMODEL,WEUSETHETENDAYMEANAIRTEMPERATUREOFCHICKLINGSTAGEANDADULTCHICKENSTAGEASINPUTS,ANDSETTHERATEFORSALEASOUTPUTFIG5SHOWSTHEREALOBSERVEDVALUESANDPREDICTEDRATEFORSALEFORBOTHMLRANDNEURALNETWORKLABELEDAS“NN”INFIG5METHODS,USINGTHETESTINGDATAFIGURE5COMPARSIONOFMLRANDNEURALNETWORKINPREDICTIONTABLE2SHOWSTHESTATISTICSFORTHEMLRANDNEURALNETWORKFORPREDICTINGBROILERRATEFORSALETABLEIIMODELSTATISTICSFORMLRANDNEURALNETWORKFORPREDICTINGRATEFORSALEMODELSTATISTICMPEMSEMLR0524328E05NEURALNETWORK0473538E05ASWECANSEEFROMTABLE2,NEURALNETWORKMODELOUTPERFORMSMLRMODELINBOTHMPEANDMSEBUTFROMTHERESULT,WECANSEETHEMLRMODELALSOHASGOODPREDICTIONPERFORMANCESIVCONCLUSIONSINTHISPAPER,WEHAVEDEALTWITHTHERESEARCHOFTHEDATAFITTINGFORBROILERGROWTHPERFORMANCEPARAMETERSGRADUALADVANCINGAN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