简介：Arobustneuralnetworkcontroller(NNC)ispresentedfortrackingcontrolofunderwatervehicleswithuncertainties.ThecontrollerisobtainedbyusingbacksteppingtechniqueandLyapunovfunctiondesignincombinationwithneuralnetworkidentification.Modelingerrorsandenvironmentaldisturbancesareconsideredinthemathematicalmodel.Atwo-layerneuralnetworkisintroducedtocompensatethemodelingerrors,whileH∞controlstrategyisusedtoachievetheL2-gainperformance.Theuniformlyultimatelybounded(UUB)stabilitiesoftrackingerrorsandNNweightsareguaranteedthroughtheproposedcontroller.Anon-lineNNweightstuningalgorithmisalsoproposed.Goodperformancesofthetrackingcontrolsystemareillustratedbytheresultsofnumericalsimulations.

简介：Aparallelneuralnetwork-basedcontroller(PNNC)ispresentedforthemotioncontrolofunderwatervehiclesinthispaper.Itconsistsofareal-timepart,aself-learningpartandadesired-stateprogrammer,anditisdifferentfromnormaladaptiveneuralnetworkcontrollerinstructure.Owingtotheintroductionoftheself-learningpart,on-linelearningcanbeperformedwithoutsampledatainseveralsampleperiods,resultinginhighlearningspeedofthecontrollerandgoodcontrolperformance.Thedesired-stateprogrammerisutilizedtoobtainbetterlearningsamplesoftheneuralnetworktokeepthestabilityofthecontroller.Thedevelopedcontrollerisappliedtothe4-degreeoffreedomcontroloftheAUV'IUV-IV'andissuccessfulonthesimulationplatform.Thecontrolperformanceisalsocomparedwiththatofneuralnetworkcontrollerwithdifferentstructuressuchasnormaladaptiveneuralnetworkanddifferentlearningmethods.Currenteffectsandsurgevelocitycontrolarealsoincludedtodemonstratethecontroller'sperformance.ItisshownthatthePNNChasagreatpossibilitytosolvetheproblemsinthecontrolsystemdesignofunderwatervehicles.

简介：AnewanalyticalmethodusingBack-Propagation(BP)artificialneuralnetworkandkineticspectrophotometryforsimultaneousdeterminationofironandmagnesiumintapwater,theYellowRiverwaterandseawaterisestablished.Byconditionalexperiments,theoptimumanalyticalconditionsandparametersareobtained.Levenberg-Marquart(L-M)algorithmisusedforcalculationinBPneuralnetwork.Thetopologicalstructureofthree-layerBPANNnetworkarchitectureischosenas15-16-2(nodes).Theinitialvalueofgradientcoefficientμisfixedat0.001andtheincreasefactorandreductionfactorofμtakethedefaultvaluesofthesystem.ThedataareprocessedbycomputerswithourownprogramswritteninMATLAB7.0.Therelativestandarddeviationofthecalculatedresultsforironandmanganeseis2.30%and2.67%respectively.Theresultsofstandardadditionmethodshowthatforthetapwater,therecoveriesofironandmanganeseareintherangesof98.0%-104.3%and96.5%-104.5%,andtheRSDisintherangeof0.23%-0.98%;fortheYellowRiverwater(LijindistrictofShandongProvince),therecoveriesofironandmanganeseareintherangesof96.0%-101.0%and98.7%-104.2%,andtheRSDisintherangeof0.13%-2.52%;fortheseawaterinQingdaooffshore,therecoveriesofironandmanganeseareintherangesof95.3%-104.8%and95.3%-104.7%,andtheRSDisintherangeof0.14%-2.66%.Itisfoundthat21commoncationsandanionsdonotinterferewiththedeterminationofironandmanganeseundertheoptimumexperimentalconditions.Thismethodexhibitsgoodreproducibilityandhighaccuracyinthedeterminationofironandmanganeseandcanbeusedforthesimultaneousdeterminationofironandmanganeseintapwaterandnaturalwater.ByusingtheestablishedANN-catalyticspectrophotometricmethod,theironandmanganeseconcentrationsofthesurfaceseawaterat11sitesinQingdaooffshorearedeterminedandtheleveldistributionmapsofironandmanganesearedrawn.

简介：Thegeophysicalmodelfunction(GMF)describestherelationshipbetweenbackscatteringandseasurfacewind,sothatwindvectorscanberetrievedfrombackscatteringmeasurement.TheGMFplaysanimportantroleinoceanwindvectorretrievals,itsperformancewilldirectlyinfluencetheaccuracyoftheretrievedwindvector.Neuralnetwork(NN)approachisusedtodevelopaunifiedGMFforC-bandandKu-band(NN-GMF).EmpiricalGMFCMOD4andQSCAT-1areusedtogeneratethesimulatedtrainingdata-set,andGaussiannoiseatasignalnoiseratioof30dBisaddedtothedata-settosimulatethenoiseinthebackscatteringmeasurement.TheNN-GMFemploysradiofrequencyasanadditionalparameter,soitcanbeappliedforbothC-bandandKu-band.Analysesshowthattheσ0predictedbytheNN-GMFiscomparablewiththeσ0predictedbyCMOD4andQSCAT-1.AlsothewindvectorsretrievedfromtheNN-GMFandempiricalGMFCMOD4andQSCAT-1arecomparable,indicatingthattheNN-GMFisaseffectiveastheempiricalGMF,andhastheadvantagesoftheuniversalform.

简介：这份报纸论述基于的一个人工的神经网络(ANN)能被用来预言c-的失败概率的反应表面方法与空间地可变的土壤倾斜。在这个方法，拉丁hypercube采样技术被采用为建立一个ANN模型产生输入数据集；随机的有限元素方法然后被利用就土壤性质的空间可变性而言计算相应输出数据集；并且最后，一个ANN模型被训练构造失败概率的反应表面并且获得合并相关变量的近似功能。说明例子的结果显示建议方法提供失败概率的可信、精确的评价。作为结果，获得的近似函数能在c-斜坡可靠性分析被用作特定的分析进程的一种选择。

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简介：Oceanunderwaterexplorationisapartofoceanographythatinvestigatesthephysicalandbiologicalconditionsforscientificandcommercialpurposes.Andvideotechnologyplaysanimportantroleandisextensivelyappliedforunderwaterenvironmentobservation.Differentfromtheconventionalmethods,videotechnologyexplorestheunderwaterecosystemcontinuouslyandnon-invasively.However,duetothescatteringandattenuationoflighttransportinthewater,complexnoisedistributionandlowlightconditioncausechallengesforunderwatervideoapplicationsincludingobjectdetectionandrecognition.Inthispaper,weproposeanewdeepencoding-decodingconvolutionalarchitectureforunderwaterobjectrecognition.Itusesthedeepencoding-decodingnetworkforextractingthediscriminativefeaturesfromthenoisylow-lightunderwaterimages.Tocreatethedeconvolutionallayersforclassification,weapplythedeconvolutionkernelwithamatchedfeaturemap,insteadoffullconnection,tosolvetheproblemofdimensiondisasterandlowaccuracy.Moreover,weintroducedataaugmentationandtransferlearningtechnologiestosolvetheproblemofdatastarvation.Forexperiments,weinvestigatedthepublicdatasetswithourproposedmethodandthestate-of-the-artmethods.Theresultsshowthatourworkachievessignificantaccuracy.Thisworkprovidesnewunderwatertechnologiesappliedforoceanexploration.

简介：Owingtothelongpropagationdelayandhigherrorrateofacousticchannels,itisverychallengingtoprovidereliabledatatransferforunderwatersensornetworks.Moreover,networkcodingisprovedtobeaneffectivecodingtechniqueforthroughputandrobustnessofnetworks.Inthispaper,weproposeaReliableBraidedMultipathRoutingwithNetworkCodingforunderwatersensornetworks(RBMR-NC).Disjointmulti-pathalgorithmisusedtobuildindependentactualpaths,ascalledmainpaths.Somebraidedpathsoneachmainpatharebuiltaccordingtothebraidedmulti-pathalgorithm,whicharecalledlogicpaths.Whenadatapacketistransmittedbythesenodes,thenodescanemploynetworkcodingtoencodepacketscomingfromthesamegroupinordertofurtherreducerelativityamongthesepackets,andenhancetheprobabilityofsuccessfuldecodingatthesinknode.Braidedmulti-pathcanmakethemainpathstobemultiplexedtoreducetheprobabilityoflongpaths.ThispapermainlyemployssuccessfuldeliveryratetoevaluateRBMR-NCmodelwiththeoreticalanalysisandsimulationmethods.TheresultsindicatethattheproposedRBMR-NCprotocolisvaluabletoenhancenetworkreliabilityandtoreducesystemredundancy.