基于自相关函数的模糊时间序列模型的优化算法

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1、万方数据英文摘要AnOptimizationAlgorithmforFuzzyTimeSeriesModelBasedonAutocorrelationFunctionAbstractFuzzytimesseriesmodelisapowerfultooltodealwiththehistoricaldatawhicharelinguisticvMuesanduncertaincircumstances，moreandmoreresearchersfocusonfuzzytimeseriesmodel．Atpresent，fuzzytimeseriesmodelhasbeenappliedt

2、oforecastthenumberoftourists，temperature，stockmarket，networkusers，incidenceofhemorrhagicfeverwithrenalsyndromeetc．Inordertogethigherprecision，differentfuzzytimeseriesmethodsareproposed．Moststudiesfocusontheuniverseofdiscoursepartitionanddatafuzzificationinfuzzytimeseriesmodels．Fuzzytimeseriesisevol

3、vedfromtheclassictimeseries，inwhichdatacorrelationisinvolvedinevitably．Thecorrelationwouldbenotchangedthroughdatafuzzification，resultingintheantecedentoffuzzyrulesinfluencingontheconsequentoffuzzyrulesinadifferentway,Thenthepaperproposesanewoptimizationalgorithmtooptimizefuzzyinferencerulesbycombin

4、ingtheautocorrelationtheoryoftimeseries．Firstly,stationarityofdataisdiscussedbyvirtueoftimeseriestheory．Ameannon。stationarysequenceistransformedintoastationarysequencethroughappropriatedifference．Inaddition，appropriatevariancestabilizingtransformationisrequiredforvariancenon—stationarysequence．Gene

5、rally,forthemeanandvariancenon—stationaryprocess，thelogarithmictransformationisappliedfirstlyandthenthedifferencetransformationisapplied．Basedonabovestep，fuzzysetsareobtainedthroughtraditionalfuzzysetsontheuniverseofdiscourseU，therebyfuzzyrulesareconstructed；Secondly,fuzzyrulesareoptimizedbyautocor

6、relationtheory．Atthesametime，animprovedmethodofstandardizeddeviationisestablishedforweightcalculation；Finally,throughcombiningwiththeprevioustimeseriesmodelandtraditionalfuzzytimeseriesmodelabouttheforecastingofAlabamauniversityenrollments，thecomparativeresultsshowthattheproposedmethodiseffective．万

7、方数据Keywords：Fuzzytimeseries；Autocorrelationfunctions；RulesWeights；Characteristicexpansionmethod．万方数据目录第l章绪论⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯11．1引言⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯．11．2存在的问题与本文的主要工作⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯