NARX神经网络多步和单步预测.pdf

% Solve an Autoregression Problem with External Input with a NARX Neural Network解决一个NARX神经网络的外部输入自回归问题 % Script generated by Neural Time Series app脚本由神经时间序列程序生成 % Created 28-Mar-2017 10:42:15 % % This script assumes these variables are defined:此脚本假设以下变量已定义： % % in - input time series.in-输入时间序列 % out - feedback time series.out-反馈时间序列 %tonndata/fromnndata把数据转化为标准神经网络格式 X = tonndata(in,false,false); T = tonndata(out,false,false); % Choose a Training Function选择一个训练功能 % For a list of all training functions type: help nntrain % trainlm is usually fastest. % trainbr takes longer but may be better for challenging problems. % trainscg uses less memory. Suitable in low memory situations. trainFcn = trainlm; % Levenberg-Marquardt backpropagation. % Create a Nonlinear Autoregressive Network with External Input inputDelays = 1:2; feedbackDelays = 1:2; hiddenLayerSize = 10; net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize,open,trainFcn); % Choose Input and Feedback Pre/Post-Processing Functions选择输入和反馈前/ 后处理功能 % Settings for feedback input are automatically applied to feedback output 反馈输入的设置将自动应用于反馈输出 % For a list of all processing functions type: help nnprocess % Customize input parameters at: net.inputs{i}.processParam删除矩阵的行定值。 映射矩阵行的最小值和最大值为[ 1 - 1 ] % Customize output parameters at: net.outputs{i}.processParam删除矩阵的行定 值。映射矩阵行的最小值和最大值为[ 1 - 1 ] net.inputs{1}.processFcns = {removeconstantrows,mapminmax}; net.inputs{2}.processFcns = {removeconstantrows,mapminmax}; % Prepare the Data for Training and Simulation准备数据用于训练和模拟 % The function PREPARETS prepares timeseries data for a particular network, preparets功能，为一个特定的网络准备时间序列数据， % shifting time by the minimum amount to fill input states and layer % states. Using PREPARETS allows you to keep your original time series data % unchanged, while easily customizing it for networks with differing % numbers of delays, with open loop or closed loop feedback modes. 移动时间的最小量从而填写输入状态和层的状态