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1、FoodControl78(2017)33e42ContentslistsavailableatScienceDirectFoodControljournalhomepage:www.elsevier.com/locate/foodcontEarlywarningmodelingandanalysisbasedonanalytichierarchyprocessintegratedextremelearningmachine(AHP-ELM):Applicationtofoodsafetya,ba,bca,b,
2、*ZhiQiangGeng,ShanShanZhao,GuangCanTao,YongMingHanaCollegeofInformationScience&Technology,BeijingUniversityofChemicalTechnology,Beijing100029,ChinabEngineeringResearchCenterofIntelligentPSE,MinistryofEducationinChina,Beijing100029,ChinacGuizhouAcademyofTesti
3、ngandAnalysis,Guiyang,Guizhou550002,ChinaarticleinfoabstractArticlehistory:Sincetheactualfoodsafetymonitoringdatahavecharacteristicsofhigh-dimension,complexity,Received27October2016discretenessandnonlinearproperties,itisdifficulttoaccuratelypredicttheriskofac
4、tualfoodinspectionReceivedinrevisedformprocess.Therefore,thispaperproposesapredictivemodelingapproachbasedonanalytichierarchy25January2017process(AHP)integratedextremelearningmachine(ELM)(AHP-ELM).TheproposedapproachutilizesAccepted19February2017theAHPmodelt
5、oobtaintheeffectiveprocesscharacteristicinformation(PCIs).ComparedwiththeAvailableonline20February2017analytichierarchyprocess(AHP)integratedtraditionalartificialneuralnetwork(ANN)approach,theAHP-ELMpredictionmodeliseffectivelyverifiedbyexecutingalinearcompari
6、sonbetweenallPCIsandKeywords:theeffectivePCIsthroughdailyinspectiondatasourcefromthesupervisionandinspectiondepartmentFoodsafetyExtremelearningmachinerepositoryofChinaqualitysupervisionsystem.Finally,thePCIsandthepredictionvalueareobtainedtoAnalytichierarchy
7、processprovidemorereliablefoodinformationandidentificationofpotentiallyemergingfoodsafetyissues.TheArtificialneuralnetworkproposedmethodisappliedtothefoodsafetyearlywarningandmonitoringsysteminChina.TheresultEarlywarningmodelingshowsthattheproposedmodeliseffec
8、tiveandfeasibleinprocessingthecomplexfoodinspectiondata.Meanwhile,itcanhelptoimprovethequalityoffoodproducts,ensurefoodsafetyandreducetheriskoffoodsafety.©2017ElsevierLtd.Allrightsreserved.1.Int
