资源目录
压缩包内文档预览：
编号：7080586
类型：共享资源
大小：1.99MB
格式：RAR
上传时间：2018-01-08
上传人：优***
认证信息
个人认证
罗**（实名认证）
广西
IP属地：广西
45
积分
- 关 键 词：
-
苹果
自动
分拣
机械
系统
设计
- 资源描述：
-
- 内容简介：
-
AVAILABLEATWWWSCIENCEDIRECTCOMVIERRTECHNIQUESFORAPPLESORTINGIKAVDIRA,C3,DEGUYERBADEPARTMENTOFAGRICULTURALMACHINERY,CANAKKALEBDEPARTMENTOFBIOSYSTEMSANDAGRICULTURALARTICLEINFOARTICLEHISTORYRECEIVED4NOVEMBER2006ACCEPTED24SEPTEMBER2007AVAILABLEONLINE28NOVEMBER2007NOLOGYHANDLINGSYSTEMS,CONSISTENCYISTHEMOSTIMPORTANTUIREALGORITHMSHAVEBEENSTUDIEDFORCLASSIFICATIONOFAGRICULTURALARTICLEINPRESSBIOSYSTEMSENGINEERING992008211219C3CORRESPONDINGAUTHORADVANTAGETHEARTIFICIALCLASSIFIERSPROVIDEINCLASSIFICATIONOFAGRICULTURALCOMMODITIESINADDITION,THEADVANTAGESOFAUTOMATEDCLASSIFICATIONOPERATIONSOVERCONVENTIONALMANUALPRODUCTSTHENUMBEROFFEATURESPLAYSAKEYROLEINDETERMININGTHEEFFICIENCYOFTHEPATTERNCLASSIFICATIONINTERMSOFTIMEANDACCURACY15375110/SEEFRONTMATTERLUOETAL,1999INBOTHSTUDIES,NONPARAMETRICCLASSIFICATIONAPPROACHESPERFORMEDBETTERCOMPAREDTOSTATISTICALMETHODSALTHOUGHTHEDIFFERENCEWASNOTSIGNIFICANTINTHEPOTATOCLASSIFICATIONSTUDYKIRSTENETAL,1997KIMETAL2000APPLIEDLINEARANDNONLINEARRECOGNITIONMODELSFORCLASSIFICATIONOFFRUITVARIOUSFEATUREEXTRACTIONANDDIMENSIONALITYREDUCTIONTECHNIQUESWEREPERFORMEDONTHESPECTRALDATAOBTAINEDFROMVISIBLEANDNEARINFRAREDWMCLASSMXIJORIGINALVALUEOFTHEJTHFEATUREOFITHPATTERNXIJNORMALISEDVALUEOFTHEJTHFEATUREOFITHPATTERNEESTIMATEOFTRUEERRORRATEMIMEANOFCLASSISICOVARIANCEMATRIXFORCLASSISUBSCRIPTSIINDEXFORTHETESTPATTERNSJNUMBEROFFEATURESKINDEXFORTHETRAININGPATTERNS992008211219SPECTRALINEARPATTERNRECOGNITIONTECHNIQUESSUCHASLINEARDISCRIMINANTANALYSISLDAANDNONLINEARTECHNIQUESBASEDONMULTILAYERPERCEPTRONSMLPSWEREUSEDTOCLASSIFYTHEPRODUCTSINTHERESULTS,NONLINEARAPPROACHESPRODUCEDSUPERIORCLASSIFICATIONRESULTSPENZAETAL2001USEDPATTERNRECOGNITIONTECHNIQUESTOCLASSIFYFOOD,BEVERAGESANDPERFUMESSUCCESSFULRESULTSWEREOBTAINEDWITHPCAANDCLUSTERANALYSISMETHODSLEEMANSETAL2002DEVELOPEDANONLINEFRUITGRADINGSYSTEMBASEDONEXTERNALQUALITYFEATURESOFAPPLESUSINGQUADRATICDISCRIMINANTANALYSISANDNNSBOTHGRADINGALGORITHMSRESULTEDINSIMILARRESULTS79AND72FORBOTHVARIETIESSTUDIEDSIMILARLY,HAHNETAL2004USEDDISCRIMINANTANALYSISANDNNSTODETECTRHIZOPUSSTOLONIFERSPORESONTOMATOESUSINGSPECTRALREFLECTANCETHENNCLASSIFIEROUTPERFORMEDTHEDISCRIMINANTANALYSISAPPROACHCIRCUMFERENCEMEASURINGDEVICECRANTONMACHINERYCOWEIGHTWASMEASUREDUSINGANELECTRONICSCALEMODELNO33CLASSIFICATIONALGORITHMS331PREPROCESSINGOFDATAANDFEATURESELECTIONWHEREXIJISTHEORIGINALVALUEOFTHEJTHFEATUREOFTHEITHPATTERN,X0IJTHENORMALISEDVALUEOFTHEJTHFEATUREOFTHEITHPATTERN,MJTHEMEANOFTHEJTHFEATURE,SJTHESTANDARDDEVIATIONOFTHEJTHFEATUREMJ1NXNI1XIJ2ANDSJ1NXNI1XIJC0MJ23THREEFEATURESETSWEREUSEDINTHECLASSIFICATIONAPPLICATIONTWODIFFERENTSUBGROUPSWITHFOURANDFIVEFEATURESARTICLEINPRESSCT1200SSERIALNO3403,CAPACITY1200701GPROGRAMMINGFORTHECLASSIFIERSWASDONEINMATLAB32DATACOLLECTIONANDHANDLINGTHENUMBEROFAPPLESUSEDFOREACHCLASSWASDETERMINEDBASEDONTHEAVAILABILITYOFSPECIALLYFEATUREDAPPLESINTHESETOFAPPLESCOLLECTEDFORTHISSTUDYTHETOTALNUMBEROFAPPLESWAS181WHICHINCLUDEDTHREECLASSESASBADCLASS3,MEDIUMCLASS2ANDGOODCLASS1QUALITYTHESIZEOFTHEPATTERNMATRIXWAS181C29WHERENINEREPRESENTEDTHENUMBEROFFEATURESEIGHTYOFTHEAPPLESWEREKEPTATROOMTEMPERATUREFORFOURDAYSAFTERHARVESTWHILEANOTHER80WEREKEPTINACOOLERATABOUT31CFORTHESAMEPERIOD,WITHOUTAPPLYINGANYQUALITYPRESORTING,TOCREATECOLOURVARIATIONONTHESURFACESOFAPPLESINADDITION,21OFTHEAPPLESWEREHARVESTEDBEFORETHEOTHERSANDKEPTFORAFURTHER15DAYSATROOMTEMPERATUREFORTHEPURPOSEOFCREATINGAVARIATIONINTHEAPPEARANCEOFTHEAPPLESTOBETESTEDAPPLESWEREGRADEDFIRSTBYAHUMANEXPERTANDTHENBYTHECLASSIFICATIONALGORITHMSDEVELOPEDTHEEXPERTWASTRAINEDONTHEEXTERNALQUALITYCRITERIAOFAPPLESFORGOOD,MEDIUMANDBADAPPLEGROUPSDEFINEDBYUSDASTANDARDSUSDA,1976THEUSDASTANDARDSFORAPPLEQUALITYEXPLICITLYDEFINETHEQUALITYCRITERIASOTHATITISQUITESTRAIGHTFORWARDFORANEXPERTTOFOLLOWUPANDAPPLYTHEMEXTREMELYLARGEORSMALLAPPLESWEREALREADYEXCLUDEDBYTHEHANDLINGPERSONNELAPPLESWEREGRADEDBYTHEHUMANEXPERTINTOTHREEQUALITY3MATERIALSANDMETHODS31DATAACQUISITIONNINEFEATURESWEREMEASUREDFROMGOLDENDELICIOUSAPPLESTHESEWEREHUEANGLEFORCOLOUR,SHAPEDEFECT,CIRCUMFERENCE,FIRMNESS,WEIGHT,BLUSHREDNATURALSPOTSONTHESURFACEOFTHEAPPLEPERCENTAGE,RUSSETANATURALNETLIKEFORMATIONONTHESURFACEOFANAPPLE,BRUISECONTENTANDNUMBEROFNATURALDEFECTSFIRMNESSWASMEASUREDUSINGAMAGNESSTAYLORMTTESTERAPPLYINGAN11MMDIAMETERPROBEINTOABOUTAN8MMDEPTHEFFEGIMCCORMICK,YAKIMAFT327COLOURWASMEASUREDUSINGACR200MINOLTACOLORIMETERINTHEDOMAINOFL,AANDB,WHERELISTHELIGHTNESSFACTORANDAANDBARETHECHROMATICITYCOORDINATESOZERETAL,1995THEHUEANGLETANC01B/A,WHICHWASUSEDTOREPRESENTTHECOLOUROFAPPLES,WASSHOWNTOBETHEBESTREPRESENTATIONOFHUMANRECOGNITIONOFCOLOURHUNGETAL,1993THESIZESOFTHESURFACEDEFECTSNATURALANDBRUISESONAPPLESWEREDETERMINEDUSINGASPECIALFIGURETEMPLATE,WHICHCONSISTEDOFANUMBEROFHOLESOFDIFFERENTDIAMETERSINADDITION,ASHAPEDEFECTLOPSIDEDNESSWASMEASUREDUSINGAMITUTOYAELECTRONICCALLIPERMITUTOYACORPORATIONANDTAKINGTHERATIOOFTHEMAXIMUMHEIGHTOFTHEAPPLETOTHEMINIMUMHEIGHTTHEMAXIMUMCIRCUMFERENCEWASMEASUREDUSINGACRANTONBIOSYSTEMSENGINEERINGGROUPSDEPENDINGONTHEEXPERTSEXPERIENCE,EXPECTATIONSANDUSDASTANDARDSUSDA,1976THENUMBERSOFAPPLESDETERMINEDFOREACHQUALITYGROUPBYTHEHUMANEXPERTAREEACHFEATUREWASNORMALISEDUSINGEQ1BELOWTOELIMINATETHEUNITDIFFERENCEBETWEENTHEMX0IJXIJC0MJSJ,1DIFFERENTCLASSIFICATIONALGORITHMSWEREAPPLIEDTOCLASSIFYAPPLESAFTERMEASURINGTHEQUALITYFEATURESANDGRADINGOFTHEAPPLESBYTHEEXPERTTHEPERFORMANCEOFTHECLASSIFIERSWASCOMPAREDWITHONEANOTHERANDTHEEXPERTPARAMETRICANDNONPARAMETRICCLASSIFIERSWEREUSEDPARAMETERESTIMATIONINTHEPARAMETRICAPPROACHWASDONEUSINGMAXIMUMLIKELIHOODESTIMATIONESTIMATEDPARAMETERSWERETHENPUTINTHEPLUGINDECISIONRULEPDRFORTHENONPARAMETRICAPPROACH,THEKNNDECISIONRULEWITHDIFFERENTVALUESOFK1,2,3,ADECISIONTREEDTCLASSIFIERWHICHWASFORMEDINTHESPLUSVENABLESXI2XID12PD2JSIJ12EXPC012XC0MITSC01IXC0MIC138C26C27,4WHEREDISTHENUMBEROFMEASUREMENTSANDTHEEXPRESSIONEXPFC012XC0MITSC01IXC0MIC138GISTHEMAHALANOBISDISTANCEARTICLEINPRESSSECONDDISCRIMINANTVARIABLE4202101GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGMMMMMMMMMMMMMMMMMMMMMMBBBBBSECONDPRINCIPALCOMPONENT2042102GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGMMMMMMMMMMMMMMMMMMMMMMBBBBB232FIRSTDISCRIMINANTVARIABLE2FIRSTPRINCIPALCOMPONENT1FIG1VISUALISATIONOFTHE3QUALITYCLASSESOFAPPLESINTWODIMENSIONALPROJECTIONSOFAFOURDIMENSIONALFEATURESPACETHETRAININGDATAWERETRANSFORMEDUSINGBOTHLINEARDISCRIMINANTANALYSISANDPRINCIPALCOMPONENTANALYSIS,ANDTHENTHE3ANALBIOSYSTEMSENGINEERING992008211219214FIRST2LINEARDISCRIMINANTVARIABLESANDFIRST2PRINCIPALCOMPONQUALITYAPPLEM,MEDIUMCLASS2QUALITYAPPLEB,BADCLASSSECONDDISCRIMINANTVARIABLE420FIRSTDISCRIMINANTVARIABLE2102GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGMMMMMMMMMMMMMMMMMMMMMBBBBB213FIG2VISUALISATIONOFTHE3QUALITYCLASSESOFAPPLESINTWODIMETRAININGDATAWERETRANSFORMEDUSINGBOTHLINEARDISCRIMINANT2LINEARDISCRIMINANTVARIABLESANDFIRST2PRINCIPALCOMPONENTSAPPLEM,MEDIUMCLASS2QUALITYAPPLEB,BADCLASS3QUALITYENTSWEREPLOTTEDFORQUALITYGROUPSG,GOODCLASS1QUALITYAPPLESECONDPRINCIPALCOMPONENT20432102GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGMMMMMMMMMMMMMMMMMMMMMBBBBBFIRSTPRINCIPALCOMPONENT21NSIONALPROJECTIONSOFAFIVEDIMENSIONALFEATURESPACETHEYSISANDPRINCIPALCOMPONENTANALYSIS,ANDTHENTHEFIRSTWEREPLOTTEDFORQUALITYGROUPSG,GOODCLASS1QUALITYAPPLEARTICLEINPRESSMEASUREMENTTHAT,TOGETHERWITHTHEEXPONENTIALCOMPONENTINTHEGAUSSIANDISTRIBUTIONFUNCTION,MAKESTHEPROBABILITYOFAPATTERNSMALLIFITISFARFROMTHEMEANPRIORIPROBABILITIESOFPW1,PW2ANDPW3WEREDETERMINEDUSINGTRAININGSAMPLESFROMEACHCLASSUNKNOWNPARAMETERSOFMIANDSI,WHICHARETHEMEANANDCOVARIANCEMATRIXFORCLASSI,WEREESTIMATEDFROMTRAININGSAMPLESUSINGTHETECHNIQUEOFMAXIMUMLIKELIHOODESTIMATIONGIVENASFOLLOWSMIC01NXNIXI,5SIC01NIXNIXC0MI26USINGTHESEPARAMETERS,THEDISCRIMINANTFUNCTIONFOREACHSECONDDISCRIMINANTVARIABLE864202GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGMMMMMMMMMMMMMMMMMMMMMBBBBB2FIRSTDISCRIMINANTVARIABLE024FIG3VISUALISATIONOFTHE3QUALITYCLASSESOFAPPLESINTWODIMENSIOTHETRAININGDATAWERETRANSFORMEDUSINGBOTHLINEARDISCRIMINANTFIRST2LINEARDISCRIMINANTVARIABLESANDFIRST2PRINCIPALCOMPONENQUALITYAPPLEM,MEDIUMCLASS2QUALITYAPPLEB,BADCLASS3BIOSYSTEMSENGINEERINGCLASSWASCALCULATEDUSINGEQ7GIXIC012XIC0MITSC01IXIC0MIC012LNSIC12C12C12C12LNPWI7USINGTHEVALUEOFGIXIINTHEDECISIONRULEGIVENBELOW,THETESTPATTERNWASASSIGNEDTOTHECLASSWHOSEDISCRIMINATIONFUNCTIONOUTPUTTHEHIGHESTVALUETHEDISCRIMINATIONFUNCTIONGMXIFORCLASSWMISGIVENASFOLLOWSASSIGNXITOCLASSWMIFGMXI4GKXIFORALLMAK,8WHEREMANDKARETHECLASSESFORXITOBEASSIGNED333KNEARESTNEIGHBOURKNNCLASSIFIERTHENEARESTNEIGHBOURCLASSIFIERISANONPARAMETRICCLASSIFIERWHICHDOESNOTMAKEANYASSUMPTIONSONTHEFORMOFTHECONDITIONALDENSITYOFACLASSANDASSIGNSTHEPATTERNTHELABELMOSTFREQUENTLYREPRESENTEDAMONGTHEKNEARESTSAMPLESINTHETRAININGSETALCHANATISETAL,1993KREPRESENTSTHENUMBEROFNEARESTNEIGHBOURSTOASSIGNATESTPATTERNACLASS,THEENTIRETRAININGDATAAREUSED,IEDISTANCESBETWEENTHETESTPATTERNANDEACHOFTHETRAININGPATTERNSHAVETOBEMEASUREDALTHOUGHTRAININGOFTHISCLASSIFIERISQUITESIMPLE,ITREQUIRESALARGECOMPUTERMEMORYASITHASTOKEEPTHEINFORMATIONOFEVERYSAMPLEINTHETRAININGSETVARIOUSMETRICSAREUSEDINMEASURINGTHESIMILARITYBETWEENPATTERNSTHEEUCLIDEANDISTANCEWASUSEDTOMEASURETHEDISTANCESIMILARITYBETWEENPATTERNSINTHISSTUDYINTHE1NNCLASSIFIER,THETESTPATTERNWASASSIGNEDTOTHECLASS,WHICHCONTAINEDTHEPATTERNTRAININGTHATWASCLOSESTTOTHEMEASUREDPATTERNTESTINGALSO,2NNAND3NNCLASSIFIERSWERETESTEDINTHESECLASSIFIERS,THEPATTERNWASASSIGNEDTOTHECLASSWHICHHADTHEMAJORITYOFPATTERNSREPRESENTEDINKNNWHENEACHOFTHENEARESTNEIGHBOURPATTERNSTRAININGWASFROMADIFFERENTCLASSIN2NNAND3NNCLASSIFIERS,THETESTPATTERNWASASSIGNEDTOTHECLASSTHATHADTHECLOSESTMEMBERTOTHETESTPATTERN,IE1NNCLASSIFICATIONPROCEDUREWASAPPLIEDWHENEACHNEARESTNEIGHBOURPATTERNWASFROMADIFFERENTQUALITYCLASSIN2NNAND3NNCLASSIFIERSTHEEUCLIDEANDISTANCEISEXPRESSEDASFOLLOWSXD2312NALPROJECTIONSOFANINEDIMENSIONALFEATURESPACEANALYSISANDPRINCIPALCOMPONENTANALYSIS,ANDTHENTHETSWEREPLOTTEDFORQUALITYGROUPSG,GOODCLASS1QUALITYAPPLESECONDPRINCIPALCOMPONENT202321GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGMMMMMMMMMMMMMMMMMMMMMMBBBBB012FIRSTPRINCIPALCOMPONENT46992008211219215DEIKJ1XIC0XK245,9WHEREDISTHENUMBEROFFEATURES,DEISTHEEUCLIDEANDISTANCEINDEXBETWEENTHEPATTERNSIANDK,XIISTHEPOSITIONOFTHETESTPATTERNANDXKISTHEPOSITIONOFTHETRAININGPATTERN334DECISIONTREEDTCLASSIFIERTHENONPARAMETRICANDHIERARCHICALDTCLASSIFIERWASFORMEDBYSPLITTINGOFSUBSETSOFTHETRAININGDATAINTODESCENDANTSUBSETSBINARYTREESTRUCTUREWASUSEDTOFORMTHEDTWHICHWASGENERATEDINSPLUSVENABLESITCONSISTSOFANINPUTLAYERWITHANUMBEROFNEURONSEQUALTOTHENUMBEROFFEATURES,HIDDENLAYERSANDANOUTPUTLAYERWITHNOHIDDENLAYER,THEPERCEPTRONCANONLYPERFORMLINEARTASKSEXPERIMENTSCONTINUEDUSINGTWOHIDDENLAYERSINTHESTRUCTUREOFTHENNWITHDIFFERENTNUMBEROFNEURONSINITEIGHTNEURONSINTHEFIRSTHIDDENLAYERANDFOURNEURONSINTHESECONDHIDDENLAYERPRODUCEDTHEOPTIMALRESULTSCOMPAREDTOTHERESTOFTHETRIALANDERROREXPERIMENTSTOFINDTHEOPTIMALSTRUCTUREOFTHENNSANONLINEARTANSIGTRANSFERFUNCTIONWASUSEDINTHEHIDDENLAYERSONENEURONWASUSEDINTHEOUTPUTLAYERWITHALINEARTRANSFERFUNCTION,PURELINDIFFERENTTYPESOFFUNCTIONSWEREUSEDINTHETRIALANDERRORPROCEDURETOFINDTHEOPTIMALFUNCTIONTHATISEFFECTIVEONTHECLASSIFICATIONSUCCESSOFTHENNSTRUCTUREPURELINWASTHEMOSTEFFICIENTINTHEOUTPUTNEURONVALUESOF025AND00025WERE,RESPECTIVELY,SELECTEDASTHECOEFFICIENTSOFLEARNINGRATEANDMOMENTUMBASEDONTRIALANDERRORTHEMAXIMUMNUMBEROFITERATIONSINTRAININGWASSETTO3000THEERRORRATECONVERGENCECRITERIONFORTHENNSTOSTOPLEARNINGWAS002NNSTOOLBOXINMATLABWASUSEDTOIMPLEMENTTHECLASSIFIERHOWARDTHENUMBEROFNEURONSINTHEINPUTLAYERWEREFOUR,FIVEANDNINEALLTHEFEATURESMEASURED,RESPECTIVELY,FORTHREEDIFFERENTSETSOFFEATURESFIG4ANDTHENUMBEROFNEURONSINTHEFIRSTANDSECONDHIDDENLAYERSWASEIGHTANDFOUR,RESPECTIVELYTHENUMBEROFHIDDENLAYERSANDTHENUMBEROFNEURONSINTHEHIDDENLAYERSWERESELECTEDBASEDONTRIALANDERROREXPERIMENTSSTARTEDWITHUSINGONEHIDDENLAYERFIRST,USINGDIFFERENTNUMBEROFNEURONSINITLATER,TRIALANDERRORWEIGHTSWEIGHTS1231234FIRSTHIDDENLAYERFIG4SCHEMATICDISPLAYOFTHEMLPWEIGHTSLAYEROUTPUTLAYERERRORTARGETOUT4321ERRORESTIMATIONWASPERFORMEDONCLASSIFICATIONRESULTSOBTAINEDFROMTRAININGANDTESTINGTHECLASSIFIERSINEVALUATINGACLASSIFIER,HALFOFTHEDATAWASUSEDTOTRAINTHECLASSIFIERWHILEHALFOFITWASUSEDFORTESTINGTABLE1THEERRORRATEFORTHENNCLASSIFIERWASCALCULATEDATTHEENDOFTRAININGANDTESTINGPROCEDURES,BASEDONTHEDIFFERENCESBETWEENTARGETEDANDCALCULATEDOUTPUTSCLASSIFICATIONACCURACYFORTHECLASSIFIERSSTUDIEDWASCALCULATEDUSINGC15EN,10NEURALNETWORKCLASSIFIERWHEREEISTHEESTIMATEOFTHETRUEERRORRATE,NISTHETOTALNUMBEROFSAMPLESANDEISTHENUMBEROFMISCLASSIFIEDPATTERNS4RESULTSANDDISCUSSIONTHECLASSIFICATIONRESULTSOBTAINEDUSINGONEPARAMETRICANDFIVENONPARAMETRICCLASSIFICATIONALGORITHMSANDTHREEDIFFERENTFEATURESETSAREGIVENINTABLES2AND3ITSHOULDBENOTEDTHATTHESUCCESSESOFTHECLASSIFIERSAREBEINGCOMPAREDWITHTHECLASSIFICATIONRESULTSOBTAINEDFROMTHEHUMANEXPERTSUBJECTIVITYISINVOLVEDINTHECLASSIFICATIONPERFORMEDBYTHEHUMANEXPERTEVENTHOUGHTHEEXPERTFOLLOWEDTHEUSDASTANDARDSTHEREFORE,ITISNOTEXPECTEDTHATANEXPERTCOULDPERFORMA100CORRECTCLASSIFICATIONPOSSIBLEMEASUREMENTERRORSFROMDEVICESSHOULDALSOBECONSIDEREDALSO,THEDATAUSEDINTHISSTUDYWEREFROMTHESAMEVARIETYOFAPPLESINWHICHTHEMORPHOLOGICALPROPERTIESDIDNOTDIFFERARTICLEINPRESSTABLE3CONFUSIONMATRICESFORTHERESULTSOBTAINEDINTESTINGACLASSIFIERCLASS4FEATURES5FEATURES9FEATURES123123123142220442005680PLUGINDECISIONRULE2319012101390304004031ERRORRATE03220278027815851604060401NEARESTNEIGHBOUR2895711415703121112121ERRORRATE02440189024415851604060402NEARESTNEIGHBOUR2895711415703121112121ERRORRATE02440189024416310640064003NEARESTNEIGHBOUR2165117412020331040031080033022WTABLE2PERFORMANCESOFTHECLASSIFIERSUSINGDIFFERENTNUMBEROFFEATURESCLASSIFIERCLASSIFICATIONSUCCESS,4FEATURESA5FEATURESB9FEATURESCPLUGINDECISIONRULE6778722272221NEARESTNEIGHBOUR7556811175562NEARESTNEIGHBOUR7556811175563NEARESTNEIGHBOUR755675567333DECISIONTREECLASSIFIERD7556MULTILAYERPERCEPTRON833388899000ACOLOUR,SHAPEDEFECT,WEIGHT,RUSSETINGBCOLOUR,SHAPEDEFECT,FIRMNESS,WEIGHT,RUSSETINGCCOLOUR,SHAPEDEFECT,CIRCUMFERENCE,FIRMNESS,WEIGHT,BLUSHPERCENTAGE,RUSSETING,SIZEOFBRUISES,SIZEOFNATURALDEFECTSDUSEDONLYWITHTHESUBSETINCLUDING4FEATURESBIOSYSTEMSENGINEERING992008211219217ERRORRATE0167ANUMBERSINTHEDIAGONALFROMTHELEFTCORNERTOTHERIGHTCORNERSHOERRORRATE02441613DECISIONTREECLASSIFIERB277313ERRORRATE02441614MULTILAYERPERCEPTRON2613THENUMBERSOFFTHELEFTTOTHERIGHTDIAGONALSHOWTHENUMBEROFMISCLASSBUSEDONLYWITHFOURFEATURES02440267613062206160616010301301110100THENUMBEROFCORRECTLYCLASSIFIEDPATTERNSFORTHERELATEDCLASSWHILEIFIEDPATTERNSPATTERNSPERCLASSWASNOTEQUAL128PATTERNSINCLASS1,44NINEFEATUREDSUBSET,WHICHCONTAINEDALLTHEFEATURESBESEENTHATTHEBESTCLASSIFICATIONSWEREPERFORMEDBYTHEARTICLEINPRESSORIGINALLYMEASUREDUSINGTHEFIVEFEATUREDSUBSETOFFEATURESDECREASEDTHETIMEFORTHECLASSIFICATIONAPPLICATIONS,WHICHRESULTEDINTHESAMEORHIGHERCLASSIFICATIONSUCCESSESCOMPAREDTOUSINGNINEFEATUREDSUBSETSUSINGNINEFEATURESINTHENNCLASSIFIER,HOWEVER,RESULTEDINTHEHIGHESTCLASSIFICATIONSUCCESS42COMPARISONOFCLASSIFICATIONALGORITHMSINTERMSOFCLASSIFICATIONPERFORMANCEWHENTHECLASSIFICATIONRESULTSWERECOMPAREDINTERMSOFTHESUCCESSESOFTHECLASSIFICATIONALGORITHMS,MLPNNSYIELDEDTHEMOSTSUCCESSFULRESULTSFORALLTHEFEATURESETSSTUDIEDTHISMAYBEATTRIBUTEDTOTHEABILITYOFTHISCLASSIFIERINASSESSINGTHENONLINEARITIESBETWEENTHEINPUTFEATURESANDTHEOUTPUTCLASSESTHEMOSTSUCCESSFULRESULT90,TABLE2USINGNNSCLASSIFIERWASOBTAINEDUSINGNINEFEATURESTHEREFORE,IMPROVEDCLASSIFICATIONRESULTSCANBEEXPECTEDWITHMOREINPUTFEATURESASINTHECASEOFUSINGANINEFEATUREDINPUTSETINTHENNSCLASSIFIERHOWEVER,MLPNNSUSINGAFIVEFEATUREDSUBSETPRODUCEDACLASSIFICATIONPERFORPATTERNSINCLASS2ANDONLYNINEPATTERNSINCLASS3THISSITUATIONMAYHAVELEDTOPOORTRAININGPERFORMANCEESPECIALLYFORCLASS3DIFFERENTFEATURESETSWERETESTEDONTHECLASSIFIERSTHERESULTSOBTAINEDFROMTHESEAPPLICATIONSAREREPORTEDINTHEFOLLOWINGSECTIONS41EFFECTOFDIFFERENTFEATURESUBSETSONCLASSIFICATIONPERFORMANCEDIFFERENTSUBSETSOFNINEINITIALFEATURESWERETESTEDONTHECLASSIFIERSFOUR,FIVEANDNINEFEATUREDTABLE2INPUTSETSWEREUSEDINSIXDIFFERENTCLASSIFIERSOUTOFTHREEFEATURESETSUSED,THEFIVEFEATUREDSUBSETPRODUCEDTHEMOSTSUCCESSFULCLASSIFICATIONRESULTSINGENERALTABLE2ALTHOUGHTHERESULTSOBTAINEDUSINGDIFFERENTFEATURESETSWERECLOSETOEACHOTHER,THEFIVEFEATUREDSUBSETSHOWEDBETTERCLASSIFICATIONRESULTSCOMPAREDTOTHEFOURFEATUREDSUBSETFORTHECLASSIFICATIONAPPLICATIONSUSINGKNNCLASSIFIERS1NN,2NNAND3NN,THEFIVEFEATUREDSUBSETOFFEATURESALSOOUTPERFORMEDTHEWIDELYENOUGHTOOBTAINCLEARLYSEPARABLECLASSESINOTHERWORDS,ALARGEAMOUNTOFOVERLAPEXISTEDBETWEENTHEGROUPSASCANBESEENINFIGS13THISOVERLAPMAYNOTONLYBEDUETOTHEPHYSICALSIMILARITIESOFTHEPRODUCE,ITMAYALSOBEDUETOTHEFACTTHATTHEHUMANEXPERTFOLLOWEDASOMEWHATSUBJECTIVECLASSIFICATIONPROCEDURETHESUBJECTIVITYOFTHEEXPERTMIGHTHAVEHADANINFLUENCEONTHERESULTSDESPITETHEUSDASTANDARDSUSED,WHICHMAYBEEXPLAINEDBYTHEDIFFERENCESINTHEEXPERIENCES,LIKESANDDISLIKESTHATTHEEXPERTPOSSESSEDTHISSITUATIONMAKESTHEPROBLEMOFFRUITCLASSIFICATIONBYSOFTCOMPUTINGTECHNIQUES,ASINTHISSTUDY,MORECOMPLEXTHEREFORE,INJUDGINGTHERESULTSOFTHECLASSIFIERS,THEABOVECONCERNSSHOULDALSOBECONSIDEREDANOTHERDISADVANTAGEOFTHEDATAWASTHATTHENUMBEROFBIOSYSTEMSENGINEERING218MANCE8889CLOSETOTHATOFTHEMLPUSINGANINEFEATUREDSUBSETMLPNNSCLASSIFIERTHENNCLASSIFIERTHATUSEDALLNINEFEATURESPROVIDEDTHEBESTCLASSIFICATIONTABLE3ONLYTWOOFTHE64APPLESWEREMISCLASSIFIEDFORTHEFIRSTCLASS,WHILESIXOF22APPLESWEREMISCLASSIFIEDFORTHESECONDCLASSALTHOUGHONLYFOURTESTPATTERNSWEREAVAILABLEFORCLASSTHREE,THREETESTPATTERNSWERECLASSIFIEDCORRECTLYANDTHEMISCLASSIFIEDTESTPATTERNWASASSIGNEDTOTHENEIGHBOURCLASSTWOHAVINGALOWNUMBEROFSAMPLESFORCLASSTHREEWASADETRIMENTALSITUATIONRESULTINGFROMPOORTRAININGANDCONSEQUENTLYPOORPERFORMANCETHEMLPNNCLASSIFIERTHATUSEDFIVEINPUTFEATURESYIELDEDTHESECONDBESTCLASSIFICATIONRESULTTABLE2INWHICHTHREEOFTHETESTSAMPLESOUTOF64INTHEFIRSTCLASS,SIXOUTOF22INTHESECONDCLASSANDONEOUTOFFOURINTHETHIRDCLASSWEREMISCLASSIFIEDTABLE31NNAND2NNCLASSIFIERSUSEDWITHFIVEFEATURESPROVIDEDTHEBESTPERFORMANCESTABLE2AFTERNNCLASSIFIERSINTHESECLASSIFIERS,FOUROUTOF64APPLESWEREMISCLASSIFIEDINTOTHENEIGHBOURCLASSFORTHESECONDCLASS,ONTHEOTHERHAND,11APPLESWERECORRECTLYCLASSIFIEDWHILESEVENAPPLESWEREMISCLASSIFIEDINTOCLASS1ANDFOURAPPL
- 温馨提示:
1: 本站所有资源如无特殊说明，都需要本地电脑安装OFFICE2007和PDF阅读器。图纸软件为CAD,CAXA,PROE,UG,SolidWorks等.压缩文件请下载最新的WinRAR软件解压。
2: 本站的文档不包含任何第三方提供的附件图纸等，如果需要附件，请联系上传者。文件的所有权益归上传用户所有。
3.本站RAR压缩包中若带图纸，网页内容里面会有图纸预览，若没有图纸预览就没有图纸。
4. 未经权益所有人同意不得将文件中的内容挪作商业或盈利用途。
5. 人人文库网仅提供信息存储空间，仅对用户上传内容的表现方式做保护处理，对用户上传分享的文档内容本身不做任何修改或编辑，并不能对任何下载内容负责。
6. 下载文件中如有侵权或不适当内容，请与我们联系，我们立即纠正。
7. 本站不保证下载资源的准确性、安全性和完整性, 同时也不承担用户因使用这些下载资源对自己和他人造成任何形式的伤害或损失。
人人文库网所有资源均是用户自行上传分享，仅供网友学习交流，未经上传用户书面授权，请勿作他用。
川公网安备: 51019002004831号