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1、ANFISMethodforForecastingInternetTrafficTimeSeriesSamiraChabaaAbdelouhabZeroual,JilaliAntariDepartmentofPhysicsDepartmentofPhysicsCadiAyyadUniversity,FacultyofSciencesSemlaliaCadiAyyadUniversity,FacultyofSciencesSemlalia,BP2390,Marrakesh40001,MoroccoBP2390,Marrakesh40001,MoroccoEmail:s.c
2、habaa@ucam.ac.maAbstract-InThispaperwehaveappliedtheadaptiveneuro?e.g.short-andlong-rangedependence,self-similarityinlarge?fuzzyinferencesystem(ANFIS)whichisrealizedbyantimescaleandmultifractalinsmall-timescale[9].appropriatecombinationoffuzzysystemsandneuralnetworksforforecastingasetofi
3、nputandoutputdataofinternettrafficInthispaper,weappliedtheadaptiveneuro-fuzzyinferencetimeseries.Severalstatisticalcriteriaareappliedtoprovidethesystem(ANFIS)methodformodelinginternettraffictimeeffectivenessofthismodel.Theobtainedresultsdemonstratethatseries.theANFISmodelpresentagoodprec
4、isioninthepredictionprocessofinternettrafficintermsofstatisticalindicators.ThisII.ADAPTIVENEURO-FuzzyINFERENCESYSTEMSmodelfitswellrealdataandprovidesaneffectivedescriptionof(ANFIS)networkconditionatdifferenttimes.Systemmodelingbasedonconventionaltools(equationKeywords-ANFIS,timeseries,in
5、ternettraffic,modelingdifferential)isnotwellsuitedfordealingwithilldefinedanduncertainsystems.Bycontrast,afuzzyinferencesystememployingfuzzyif-thenrulescanmodelthequalitativeaspectsI.INTRODUCTIONofhumanknowledgeandreasoningprocesseswithoutNetworksanddistributedprocessingsystemshavebecome
6、employingprecisequantitativeanalyses[10].Theadaptiveanimportantsubstrateofmodeminformationtechnology[1].neuro-fuzzyinferencesystem(ANFIS)isrealizedbyanThesesystemsknowarapidgrowingthroughouttheworkappropriatecombinationoffuzzysystemsandneuralnetworks.placewhichhasgivenrisetoadiscontinuit
7、yinexpertiseofThishybridcombinationenablestoutilizeboththeverbalandhumanoperatorstomanagethem.numericpowerofintelligentsystems.Commonly,fromtheSeveralresearchhavedemonstratethattheactualnetworktheoryoffuzzysystems,differentfuzzificationandtrafficpresentsdynamiccharacteris
