简介：Astherearelotsofnon-linearsystemsintherealengineering,itisveryimportanttodomoreresearchesonthemodelingandpredictionofnon-linearsystems.Basedonthemulti-resolutionanalysis(MRA)ofwavelettheory,thispapercombinedthewavelettheorywithneuralnetworkandestablishedaMRAwaveletnetworkwiththescalingfunctionandwaveletfunctionasitsneurons.Fromtheanalysisinthefrequencydomain,theresultsindicatedthatMRAwaveletnetworkwasbetterthanotherwaveletnetworksintheabilityofapproachingtothesignals.AnessentialresearchwascarriedoutonmodelingandpredictionwithMRAwaveletnetworkinthenon-linearsystem.Usingthelengthwiseswaydatareceivedfromtheexperimentofshipmodel,amodelofofflinepredictionwasestablishedandwasappliedtotheshort-timepredictionofshipmotion.Thesimulationresultsindicatedthattheforecastingmodelimprovedthepredictionprecisioneffectively,lengthenedtheforecastingtimeandhadabetterpredictionresultsthanthatofARlinearmodel.TheresearchindicatesthatitisfeasibletousetheMRAwaveletnetworkintheshort-timepredictionofshipmotion.

简介：察觉弱在水下信号是对海洋的工程的一般兴趣的一个区域。一个弱信号察觉计划被开发；它联合了非线性的动态重建技术，神经网络和扩大Kalman过滤的光线的基础功能(RBF)(EKF)。在这方法混乱，理论被用来为背景噪音建模。噪音被阶段空间重建技术和RBF神经网络以一种synergistic方式预言。当一个信号不在时，预言错误保持低并且当输入包含了一个信号时，变得相对大。EKF被用来改进RBF神经网络的集中率。甚至当signal-to-noise比率(SNR)是不到?40dB时，到不同试验性的数据集合的计划的申请证明算法能检测在强壮的噪音隐藏的信号。

简介：影响的因素在水下车辆声纳自我噪音被分析，并且基因算法和背繁殖(BP)神经网络被联合预言在水下车辆声纳自我噪音。试验性的结果表明那在水下车辆sonarself噪音能被基于的一个GA-BP神经网络精确地预言实际在水下车辆声纳数据。

简介：ADRNN(diagonalrecurrentneuralnetwork)anditsRPE(recurrentpredictionerror)learningalgorithmareproposedinthispaper.UsingofthesimplestructureofDRNNcanreducethecapacityofcalculation.TheprincipleofRPElearningalgorithmistoadjustweightsalongthedirectionofGauss-Newton.Meanwhile,itisunnecessarytocalculatethesecondlocalderivativeandtheinversematrixes,whoseunbiasednessisproved.Withapplicationtotheextremelyshorttimepredictionoflargeshippitch,satisfactoryresultsareobtained.Predictioneffectofthisalgorithmiscomparedwiththatofauto-regressionandperiodicaldiagrammethod,andcomparisonresultsshowthattheproposedalgorithmisfeasible.

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