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简介：Artificalneuralnetworks(ANN)arenowwidelyusedsuccessfullyastoolsforhithenergyphysics.Thepapercoverstwoaspects.First,mappingANNsontotheproposedringandlinearsystolicarrayprovidesanefficientimplementationofVLSI-basedarchitecturessinceinthiscaseallconnectionsamongprocessingelementsarelocalandregular,Second.itisdiscussedalgorthmicorganizingofsuchstructuresonthebaseofmodularalgebrawhoseusecanprovideanessentialincreaseofsystemthroughput.

简介：WeproposeanapplicationoftheelasticneuralnetforringrecognitioninRICHdetectors.Themethodhasbeendevelopedtofindringsdistortedduetomisalignmentofdetectorsandcontaminatedbynoise.ThealgorithmwastestedonsimulateddataofCOMPASSRICH-1detector.Reconstructionefficiencyis99.95%fortripleLEPTOeventstaking5msperevent.

简介：TheneuralnetworkrealtimeeventselectionfortheDIRACewperimentatCERNispresented.Itcomprisesoftwoindependentparts.OneusesplasticscintillatorsandtheothertheverticalScintillatingFibres,Theglobaleventdecisionistakeninlessthan250ns.Signaleventsareselectedwithanefficiencyosmorethan0.99withabackgroundratereductionofabout2.

简介：我们构造由蒙特卡罗的神经网络基于的二层的反馈作为定点引起注意的人或作为限制周期引起注意的人存储记忆的算法。特殊注意集中于把网络的动力学与限制周期引起注意的人并且与定点引起注意的人作比较。前者比后者有更好的检索性质，这被发现。特别地，当记忆作为一个长限制的周期被存储时，假记忆可以完全被压制。limit-cycle-attractor网络的潜在的申请简短被讨论。

简介：IntheresearchofusingRadialBasisFunctionNeuralNetwork(RBFNN)forecastingnonlineartimeseries,weinvestigatehowthedifferentclusteringsaffecttheprocessoflearningandforecasting.Wefindthatk-meansclusteringisverysuitable.Inordertoincreasetheprecisionweintroduceanonlinearfeedbacktermtoescapefromthelocalminimaofenergy,thenweusethemodeltoforecastthenonlineartimeserieswhichareproducedbyMackey-Glassequationandstocks.Byselectingthek-meansclusteringandthesuitablefeedbackterm,muchbetterforecastingresultsareobtained.

简介：Therobustexponentialstabilityofalargerclassofdiscrete-timerecurrentneuralnetworks(RNNs)isexploredinthispaper.Anovelneuralnetworkmodel,namedstandardneuralnetworkmodel(SNNM),isintroducedtoprovideageneralframeworkforstabilityanalysisofRNNs.MostoftheexistingRNNscanbetransformedintoSNNMstobeanalyzedinaunifiedway.ApplyingLyapunovstabilitytheorymethodandS-Proceduretechnique,twousefulcriteriaofrobustexponentialstabilityforthediscrete-timeSNNMsarederived.Theconditionspresentedareformulatedaslinearmatrixinequalities(LMIs)tobeeasilysolvedusingexistingefficientconvexoptimizationtechniques.Anexampleispresentedtodemonstratethetransformationprocedureandtheeffectivenessoftheresults.

简介：Inthispaper,weproposeanovelapproachtoachievespectrumprediction,parameterfitting,inversedesign,andperformanceoptimizationfortheplasmonicwaveguide-coupledwithcavitiesstructure(PWCCS)basedonartificialneuralnetworks(ANNs).TheFanoresonanceandplasmon-inducedtransparencyeffectoriginatedfromthePWCCShavebeenselectedasillustrationstoverifytheeffectivenessofANNs.WeusethegeneticalgorithmtodesignthenetworkarchitectureandselectthehyperparametersforANNs.OnceANNsaretrainedbyusingasmallsamplingofthedatageneratedbytheMonteCarlomethod,thetransmissionspectrapredictedbytheANNsarequiteapproximatetothesimulatedresults.Thephysicalmechanismsbehindthephenomenaarediscussedtheoretically,andtheuncertainparametersinthetheoreticalmodelsarefittedbyutilizingthetrainedANNs.Moreimportantly,ourresultsdemonstratethatthismodel-drivenmethodnotonlyrealizestheinversedesignofthePWCCSwithhighprecisionbutalsooptimizessomecriticalperformancemetricsforthetransmissionspectrum.Comparedwithpreviousworks,weconstructanovelmodel-drivenanalysismethodforthePWCCSthatisexpectedtohavesignificantapplicationsinthedevicedesign,performanceoptimization,variabilityanalysis,defectdetection,theoreticalmodeling,opticalinterconnects,andsoon.

简介：Inthispaper,amulti-objectiveparticleswarmoptimization(MOPSO)algorithmandanondominatedsortinggeneticalgorithmⅡ(NSGA-Ⅱ)areusedtooptimizetheoperatingparametersofa1.6L,sparkignition(SI)gasolineengine.Theaimofthisoptimizationistoreduceengineemissionsintermsofcarbonmonoxide(CO),hydrocarbons(HC),andnitrogenoxides(NOx),whicharethecausesofdiverseenvironmentalproblemssuchasairpollutionandglobalwarming.Stationaryenginetestswereperformedfordatageneration,covering60operatingconditions.Artificialneuralnetworks(ANNs)wereusedtopredictexhaustemissions,whoseinputswerefromsixengineoperatingparameters,andtheoutputswerethreeresultingexhaustemissions.TheoutputsofANNswereusedtoevaluateobjectivefunctionswithintheoptimizationalgorithms:NSGA-ⅡandMOPSO.Thenadecision-makingprocesswasconducted,usingafuzzymethodtoselectaParetosolutionwithwhichthebestemissionreductionscanbeachieved.TheNSGA-Ⅱalgorithmachievedreductionsofatleast9.84%,82.44%,and13.78%forCO,HC,andNOx,respectively.WithaMOPSOalgorithmthereachedreductionswereatleast13.68%,83.80%,and7.67%forCO,HC,andNOx,respectively.

简介：Ameasurementtechniquethatcanmeasuretheconcentrationofthesolidparticlesinliquidflowwasdeveloped.ThemeasurementsystemconsistsofacolorcameraandthreeLCDdisplays.ThesolidparticleswereputatthebottomofacylindricalmixingtankinwhichJetA1oilwasfilled.Transientmixingofthesolidparticleswasperformedbyrotatingapropellertypeagitatorwiththreedifferentrotationspeed(500,600,700r/min).MixingstatewasvisualizedbytheLCDdisplaysandacolorcamcorder.Thecolorintensityoftheglassparticleschangeswiththeirconcentration.ThecolorinformationwasdecodedintothreeprinciplecolorsR,G,andBsothat,thecalibrationcurveofcolor-to-concentrationwasperformedusingtheseinformation.Aneuralnetworkwasusedforthiscalibration.Thetransientconcentrationfieldofthesolidparticleswasquantitativelyvisualized.

简介：连接神经原的几何距离的分发是在大脑位于神经网络下面的一个实际因素。它能影响在地面的动态性质铺平的大脑。Karbowski导出被实验还没验证了的幂定律腐烂分布。在这个工作，我们与一个现象学的模型一起用模拟检查它的有效性。基于在里面vitro在由Ito的文化容器的神经网络的二维的开发，我们匹配触处数字浸透时间为开发进程获得合适的参数，然后在如此的条件下面决定在连接神经原之间的距离的分发。我们的模拟获得清楚的指数的分布而不是幂定律，它显示那个Karbowskis结论是无效的，至少为盒子在vitro在二维的文化容器的神经网络开发。

简介：Inthispaper,weinvestigatecoherenceresonance(CR)andnoise-inducedsynchronizationinHindmarsh-Rose(HR)neuralnetworkwiththreedifferenttypesoftopologies:regular,random,andsmall-world.ItisfoundthattheadditivenoisecaninduceCRinHRneuralnetworkwithdifferenttopologiesanditscoherenceisoptimizedbyapropernoiselevel.Itisalsofoundthatascouplingstrengthincreasestheplateauinthemeasureofcoherencecurvebecomesbroadenedandtheeffectsofnetworktopologyismorepronouncedsimultaneously.Moreover,wefindthatincreasingtheprobabilitypofthenetworktopologyleadstoanenhancementofnoise-inducedsynchronizationinHRneuronsnetwork.

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简介：Thealternatecombinationalapproachofgeneticalgorithmandneuralnetwork(AGANN)hasbeenpresentedtocorrectthesystematicerrorofthedensityfunctionaltheory(DFT)calculation.IttreatstheDFTasablackboxandmodelstheerrorthroughexternalstatisticalinformation.Asademonstration,theAGANNmethodhasbeenappliedinthecorrectionofthelatticeenergiesfromtheDFTcalculationfor72metalhalidesandhydrides.ThroughtheAGANNcorrection,themeanabsolutevalueoftherelativeerrorsofthecalculatedlatticeenergiestotheexperimentalvaluesdecreasesfrom4.93%to1.20%inthetestingset.Forcomparison,theneuralnetworkapproachreducesthemeanvalueto2.56%.Andforthecommoncombinationalapproachofgeneticalgorithmandneuralnetwork,thevaluedropsto2.15%.Themultiplelinearregressionmethodalmosthasnocorrectioneffecthere.