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1、TextileResearchJournalArticleUseofArtificialNeuralNetworksforDeterminingtheLevelingActionPointattheAuto-levelingDrawFrameAssadFarooq1andChokriCherifInstituteofTextileandClothingTechnology,TechnischeUniversitätDresden.Dresden,GermanyAbstractArtificialneuraln

2、etworkswiththeirabilityoflearningfromdatahavebeensuccessfullyappliedinthetextileindustry.Thelevelingactionpointisoneoftheimportantauto-levelingparametersofthedrawingframeandstronglyinfluencesthequalityofthemanufacturedyarn.Thispaperreportsamethodofpredictin

3、gthelevelingactionpointusingartificialneuralnetworks.Variouslevelingactionpointaffectingvariableswereselectedasinputsfortrainingtheartificialneuralnetworkswiththeaimtooptimizetheauto-levelingbylimitingthelevelingactionpointsearchrange.TheLevenbergMarquardta

4、lgorithmisincorporatedintotheback-propagationtoacceleratethetrainingandBayesianregularizationisappliedtoimprovethegeneralizationofthenetworks.Theresultsobtainedarequitepromising.Keywords:artificialneuralnetworks；auto-lev-eling；drawframe；levelingactionpointT

5、heevennessoftheyarnplaysanincreasinglysignificantroleinthetextileindustry,whilethesliverevennessisoneofthecriticalfactorswhenproducingqualityyarn.Thesliverevennessisalsothemajorcriteriafortheassessmentoftheoperationofthedrawframe.Inprinciple,therearetwoappr

6、oachestoreducethesliverirregularities.Oneistostudythedraftingmechanismandrecognizethecausesforirregularities,sothatmeansmaybefoundtoreducethem.Theothermorevaluableapproachistouseauto-levelers[1],sinceinmostcasesthedoublingisinadequatetocorrectthevariationsi

7、nsliver.Thecontrolofsliverirregularitiescanlowerthedependenceoncardsliveruniformity,ambientconditions,andframeparameters.Attheauto-levelerdrawframe(RSB-D40)thethicknessvariationsinthefedsliverarecontinuallymonitoredbyamechanicaldevice(atongue-grooveroll)and

8、subsequentlyconvertedintoelectricalsignals.Themeasuredvaluesaretransmittedtoanelectronicmemorywithavariable,thetimedelayedresponse.Thetimedelayallowsthedraftbetweenthemid-rollandthedeliveryrollofthedra