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简介：Adaptivefuzzy-neural-networkcontrolforinductionspindlemotordrive；……

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简介：Arecurrentlocalmodelnetworkfor,nonlinearprocessmodeling.ARECURRENTNEURALNETWORKFORI-DPHASERETRIEVAL.ARecurrentNeuralNetworkforNonlinearConvexProgramming.ArecurrentneuralnetworkmodeloftheB.T.fed-batchfermentationprocess.ActiveControlofSoundbasedonDiagonalRecurrentNeuralNetwork.

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简介：Amodelfortheoptimisationofallfuzzy-controller-componentsbyanartificialneuralnetwork.AModularApproachforReliableNanoelectronicandVery-DeepSubmicronCircuitDesignBasedonAnalogNeuralNetworkPrinciples.AmultipointopticalevanescentwaveU-bendsensorsystembasedonartificialneuralnetworkpatternrecognition.Amultivariableapproachformappingsub-pixellandcoverdistributionsusingMISRandMODIS:ApplicationintheBrazilianAmazonregion.

简介：Anoveldailypeakloadforecastingmethodusinganalyzablestructuredneuralnetwork；Apseudo-randomtestingschemeforanalogintegratedcircuitsusingartificialneuralnetworkmodel-basedobservers；AROBUSTPOWERSYSTEMSTABILIZERCONFIGURATIONUSINGARTIFICIALNEURALNETWORKBASEDONLINEAROPTIMALCONTROL(STUDENTPAPERCOMPETITION)；ASelf-AimingCameraBasedonNeurophysicalPrinciples；Aself-organizingneuralmodelforcontext-basedactionrecognition；Asequentialfeatureselection-basedneuralnetworkapproachtodynamicvoltagestabilityestimation。

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简介：Adesignmethodfornoisecancellerusingrecurrentneuralfilter，ADRNN-baseddirectmulti-stepadaptivepredictorforintelligentsystems，Agenerationalterationmodelforevolvingrecurrentneuralnetworktopologiesalongwithweights，AHIGH-PERFORMANCEMULTI-PURPOSEDSPARCHITECTUREFORSIGNALPROCESSINGRESEARCH……

简介：AChaoticNeuralNetworkforReducingthePeak-to-AveragePowerRatioofMulticarrierModulation；Acombinatorialoptimizationmethodbychaoticneurodynamics；Amultistageself-organizingalgorithmcombinedtransientlychaoticneuralnetworkforcellularchannelassignment；AreductionmethodforPAPRinOFDMvianeuralnetworks；ASearchingAbilityofSolutionsofN-QueensProblemUsingChaotic；Atheoreticalstudyonaneuralmethodforcombinatorialoptimizationproblems；AdaptiveMemoryDynamicsinaChaoticNeuralNetwork；

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简介：[篇名]AHamilton-JacobiSetupforConstrainedNeuralNetworkControl，[篇名]ANEURALNETWORKCONTROLBASEDOBSERVERFORROBOTMANIPULATORS，[篇名]Aneuro-controllerwithguaranteedstability，[篇名]AnewRBFneuralnetworkcontrolstrategybasedonnewobjectfunction，[篇名]ANovelNeuralNetworkControllerandItsEfficientDSPImplementationforVector-ControlledInductionMotorDrives，[篇名]Asinge-phaseactivepowerfilterwithneuralnetwork-basedcontroller，[篇名]AStudyonNeuralNetworkControlofExplosion-Proof2-LinkPneumaticManipulator，[篇名]Activepowerlineconditionerwithaneuralnetworkcontrolscheme。

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简介：ApplyingthegeneticprogrammingtomodelingofdiffusionprocessesbyusingtheCNNanditsapplicationstothesynchronization.Cellularneuralnetworkanditsapplicationinthediagnosisofabnormalautomobilesound.Cellularneuralnetworkinimagefiltrationtasks.Cellularneuralnetworksanditsapplicationforabnormaldetection-optimizationofthecellularneuralnetworksbydesigninganoutputfunction.

简介：Abiologicallyinspiredconnectionistsystemfornaturallanguageprocessing，Abiologicallymotivatedconnectionistsystemforpredictingthenextwordinnaturallanguagesentences，Acombinedmodelofwaveletandnenralnetworkforshorttermloadforecasting，Acomparativestudyofradialbasisfunctionneuralnetworksandwaveletneuralnetworksinclassificationofremotelysenseddata……

简介：摘要：在供电公司负荷预测中，企业用户的电力需求预测精确度对整个地区的负荷预测至关重要，能使负荷预测更加细化、精确化，从而利于电能供给平衡。然而近年来，人工智能方法用于用电需求的预测上，仍存在网络参数容易陷入局部最优的问题。基于此，以璧山城区646户高压企业客户，连续23个月的用电量为数据源，对其进行数据挖掘、聚类分析、预测，根据不同企业用电习惯及特点，挖掘数据特征向量，基于RBF人工神经网络建企业日用电量的预测模型，并与BP神经网络企业日用电量预测模型进行对比测试，基于RBF神经网络算法的企业日用电量模型精确度高达90%。

简介：摘要：在电机的故障诊断中应用径向基 (RBF)神经网络建立起异步电机的 RBF神经网络诊断模型。由于 RBF神经网络学习算法容易陷入局部最优，我们引入粒子群优化算法（ PSO）来优化 RBF神经网络基函数的中心、宽度以及网络的连接权值、以获得最优的网络模型。仿真结果表明优化后的 RBF神经网络的泛化能力和诊断精度都得到了大幅度提高。