简介：Inthispaperamodeloftransvcrsalfilterispresentedtostudytheadaptivematchofthetimevariantchannel.Theleastmeansquareerrorfil-teringmethodisusedtoobtaintheweightingcoeffcientsofthefilter.Withthepurposeofspeedinguptheconvergenceoftheiterationequationofadaptivefiltering,anadaptivefactoroftheiterationstepsizeμ_aisderivedinthispaper.Theresultofcomputersimulationshowsthatinthecaseofusingadaptiveμ_a,theconvergencespeedoftheiterationequationisincreased2timesapproximatelyincomparisonwithconstantμ_f.Thestudysuggeststhattheadaptivefilterwithadaptiveμ_a.havetheperformancetofollowthechangeoftime-variantcharacteristicsofthechannel.

简介：复杂适应系统(cas)-包含当他们交往，适应或学习的许多部件的系统-在重要当代的问题的心。学习ofcas提出唯一的挑战：一些我们的很强大的数学工具，特别地，包含的方法修理了点，引起注意的人，等等，具有在理解cas的发展的有限帮助。这篇论文建议方法修改研究方法和工具，与基于计算机的模型的角色上的一个重音，到增加我们cas的理解。

简介：Anadaptivealgorithmforsolvinglargenonsymmetriclinearsystemsispresentedinthispaper.ThenewalgorithmcombinespolynomialpreconditioningtechniquewiththeCGNRmethod.Residualpolynomialisusedinthepreconditioningtoestimatetheeigenvaluesofthes.p.d.matrixArA,andtheresidualpolynomialisgeneratedfromseveralstepsofCGNRbyrecurrence.Thealgorithmisadaptiveduringitsimplementation.Therobustnessismaintained,andtheiterationconvergenceisspeededup.Twonumericaltestresultsarealsoreported.

简介：ＥｘｐｅｒｉｍｅｎｔａｌＳｔｕｄｉｅｓｏｆＡｄａｐｔｉｖｅＣｏｎｓｃｉｏｕｓＳｙｓｔｅｍｓＣａｉＹｉｆａ（ＤｅｐｔｏｆＭａｔｈ＆ＣｏｍｐｕｔｅｒＳｃｉｅｎｃｅｓＤｕｎｄｅｅＩｎｓｔｉｔｕｔｅｏｆＴｅｃｈｎｏｌｏｇｙＤｕｎｄｅｅＤＤ１１ＨＧＵｎｉｔｅｄ...

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简介：Responseofadaptivematchedfilter,alsocalledadaptivecorrelator,tomultipathchannelisdiscussedinthispaper.Ithasbeenprovedthatthenewtypeprocessorcanbettermatchwithmultipathchan-nel.Theresultsofexperimentcarriedoutonlakeandinlaboratoryarepresented.Itshowsthattheprocessorhasgooddetectingperformanceintimedomain.

简介：Thispaperdescribestheinverstigationdevotedtoestablishsuitableweightsinafeed-forwardneuralnetworkrealizingthenarrow-bandfilteringmapinthecaseofadaptivelineenhancement(ALE)bytheutilityoftheoptimumcommonlearningratebackpropagation(OCLRBP)algorithm.Itisfoundthatafeed-forwardnetworkwith64linearinputandoutputneurons,and8oddsigmoidneuronsinthehiddenlayer,i.e.an(64→8→64)architecture,couldestablishthespecificinput-outputfunctioninthecaseofrelativelylowsignal-to-noiseradio.Onlyisaninputsignalconsistingofmixedperiodicandbroad-bandcomponentsavailabletothenetworksystem.Afterlearning,boththe"fanning-in-connectionpatterns",eachofwhichconsistsofweightsfanningintoahidden-neuronFromalltheoutputsofinput-neurons,andthe"fanning-out-connectionpatterns",eachofwhichconsistsofweightsfanningoutfromahidden-neurontoalltheinputsofoutput-neurons,aretunedtotheperiodicsignals.Thenonline

简介：ＳＨＥＬＬＡＤＡＰＴＩＶＥＴＲＩＡＮＧＵＬＡＴＩＯＮＯＦＴＲＩＭＭＥＤＮＵＲＢＳＳＵＲＦＡＣＥＷａｎｇＨｕｉｃｈｅｎｇＺｈａｎｇＸｉｎｆａｎｇＺｈｏｕＪｉＡｂｓｔｒａｃｔＴｈｅｐａｐｅｒｐｒｅｓｅｎｔｓａｎｅｗａｐｐｒｏａｃｈｆｏｒｔｒｉａｎｇｕｌａ...

简介：Thispaperpresentanimprovedpreciseintegrationalgorithmfortransientanalysisofheattransferandsomeotherproblems.Theoriginalpreciseintegrationmethodisimprovedbymeanoftheinve-rseaccuracyanalysissothattheparameterN,whichhasbeentakenasaconstantandanindependentpa-rameterwithoutconsiderationoftheproblemsintheoriginalmethod,canbegeneratedautomaticallybythealgorithmitself.Thus,theimprovdealgorithmisadaptiveandtheaccucacyofthealgorithmisnotdependentonthelengthofthetimestepintheintegrationprocess.Itisshownthatthenumericalresultsobtainedbythemethodproposedaremoreaccuratethanthoseobtainedbytheconventionaltimeintegrationmethodssuchasthedifferencemethodandothers.Fourexamplesaregiventodemonstratethevalidity,accuracyandeffi-ciencyofthenewmethod.

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简介：FortheARMAXsystemwithunknowncoefficientstheoptimaladaptivecontrolisdesignedsothatthefollowingrequirementsaremetsimultaneously:1)thetransferfunctionfromareferencesignaltothesystemoutputintheclosedloopequalsaprescribedrationalfunction;2)undertheconstraintmentionedin1)aquadraticlossfunctionisminimized;3)theparameterestimateisstronglyconsistent.

简介：ThispaperpresentsapragmaticadaptiveschemeforTuCMoverslowlyfadingchannels.Theadaptiveschemeemploysasingleturbocodedmodulatorcomposedofavariable-rateturboencoderandavariable-ratevariable-powerMQAMforallfadingregions,soithasanacceptablecomplexitytoimplement.TheoptimaladaptiveTuCMschemeisdeterminedsubjecttovarioussystemconstraints.Simulationshavebeenperformedtomeasuretheperformanceoftheschemefordifferentparameters.Itisshownthatadoptingboththeturbocodedmodulatorandthetransmitpowerachievesaperformancewithin2.5dBofthefadingchannelcapacity.

简介：ＥｆｆｅｃｔｓｏｎＳｕｍａｎｄＤｉｆｆｅｒｅｎｃｅＰａｔｔｅｒｎｓｂｙＡｄａｐｔｉｖｅＮｕｌｌＸｉｅＬｉａｎｇｇｕｉａｎｄＪｉａｎｇＸｉｎｆａ（ＢｅｉｊｉｎｇＩｎｓｔｉｔｕｔｅｏｆＲａｄｉｏＭｅａｓｕｒｅｍｅｎｔ，Ｐ．Ｏ．Ｂｏｘ３９２３，Ｂｅｉｊｉｎ...

简介：Anewadaptivetechniqueofr-andh-versionforvibrationproblemsutilizingthematrixper-turbationtheoryandelementenergyratioisproposed.Instructuralvibrationanalysis,throughther-conver-genceadaptivefiniteelementprocess,meshoptimizationcanberealized.Inthelightofthejudgementonthechangesinthemagnitudeoftheelementenergyratio,localrefinementcanbeachievedintheprocessofh-convergenceadaptivefiniteelementsothatmoreaccuratefiniteelementsolutionscanbeobtainedwithasfewmeshesaspossible.Manynumericalexamplesaregivenandtheproposedapproachisshowntobefeasibleandeffective.

简介：Totacklemulticollinearityorill-conditioneddesignmatricesinlinearmodels,adaptivebiasedestimatorssuchasthetime-honoredSteinestimator,theridgeandtheprincipalcomponentestimatorshavebeenstudiedintensively.Tostudywhenabiasedestimatoruniformlyoutperformstheleastsquaresestimator,somesufficientconditionsareproposedintheliterature.Inthispaper,weproposeaunifiedframeworktoformulateaclassofadaptivebiasedestimators.Thisclassincludesallexistingbiasedestimatorsandsomenewones.Asufficientconditionforoutperformingtheleastsquaresestimatorisproposed.Intermsofselectingparametersinthecondition,wecanobtainalldouble-typeconditionsintheliterature.

简介：Supplychainisacomplex,hierarchical,integrated,openanddynamicnetwork.Everynodeinthenetworkisanindependentbusinessunitthatunitesotherorganizationstodevelopitsvalue,thecompetitionandcooperationbetweentheseunitsarebasicimpetusofthedevelopmentandevolutionofthesupplychainsystem.Thecharacteristicsofsupplychainasacomplexadaptivesystemanditsmodelingarediscussedinthispaper,anduseanexampledemonstratingthefeasibilityofCASmodelinginsupplychainmanagementstudy.

简介：Inthispaper,atrustregionmethodforequalityconstrainedoptlmizationbasedonnondiferentiableexactpenaltyisproposed.Inthisalgorithin,thetrailstepischaracterizedbycomputationofitsnormalcomponentbeingseparatedfromcomputationofitstangentialcomponent,i.e.,onlythetangentialcomponentofthetrailstepisconstrainedbytrustradiuswhilethenormalcomponentandtrailstepitselfhavenoconstraints.Theothermaincharacteristicofthealgorithmisthedecisionoftrustregionradius.Here,thedecisionoftrustregionradiususestheinformationofthegradientofobjectivefunctionandreducedHessian.However,Maratoseffectwilloccurwhenweusethenondifferentiableexactpenaltyfunctionasthemeritfunction.Inordertoobtainthesuperlinearconvergenceofthealgorithm,weusethetwiceordercorrectiontechnique.Becauseofthespecialityoftheadaptivetrustregionmethod,weusetwiceordercorrectionwhenp=0(thedefinitionisasinSection2)andthisisdifferentfromthetraditionaltrustregionmethodsforequalityconstrainedopthnization.Sothecomputationofthealgorithminthispaperisreduced.Whatismore,wecanprovethatthealgorithmisgloballyandsuperlinearlyconvergent.

简介：Optimisingbothqualitativeandquantitativefactorsisakeychallengeinsolvingconstructionfinancedecisions.Thesemi-structurednatureofconstructionfinanceoptimisationproblemsprecludesconventionaloptimisationtechniques.Withadesiretoimprovetheperformanceofthecanonicalgeneticalgorithm(CCA)whichischaracterisedbystaticcrossoverandmutationprobability,andtoprovidecontractorswithaprofit-risktrade-offcurveandcashflowprediction,anadaptivegeneticalgorithm(AGA)modelisdeveloped.TenprojectsbeingundertakenbyamajorconstructionfirminHongKongwereusedascasestudiestoevaluatetheperformanceofthegeneticalgorithm(CA).TheresultsofcasestudyrevealthattheACAoutperformedtheCGAbothintermsofitsqualityofsolutionsandthecomputationaltimerequiredforacertainlevelofaccuracy.TheresultsalsoindicatethatthereisapotentialforusingtheGAformodellingfinancialdecisionsshouldbothquantitativeandqualitativefactorsbeoptimisedsimultaneously.

简介：Weconsiderinthispapertheproblemofrecursiveidentificationforstochasticsystemswhenthenoisemodeldoesnotsatisfythepositiverealconditionassociatedwithconvergenceofstandardalgorithms.Toavoidthepositiverealcondition,adaptivespectralfactorizationtechniquesareexploitedonthebasisofaclassofnon-standardtime-varyingrecursiveRiccatiequations.TheasymptoticpropertiesoftheRiccatiequationsarestudiedasacrucialsteptotheconvergenceresultsofthepaper.