基于CNN(卷积神经网络)时间序列预测-NARX-MATLAB实现源码

% ------------------ Georgios Etsias 17th March 2020 ----------------- % %% Training ANN max=10^10; for ii=1:10 % Solve an Autoregression Problem with External Input with a NARX Neural Network % Script generated by Neural Time Series app % Created 17-Mar-2020 14:33:37 % % This script assumes these variables are defined: % % TrainingInput - input time series. % TrainingOutput - feedback time series. X = TrainingInput; T = TrainingOutput; % Choose a Training Function % 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 = 'trainbr'; % Bayesian Regularization backpropagation. % Create a Nonlinear Autoregressive Network with External Input inputDelays = 1:2; feedbackDelays = 1:2; hiddenLayerSize = 10; net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize,'open',trainFcn); % Prepare the Data for Training and Simulation % The function PREPARETS prepares timeseries data for a particular network, % 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. [x,xi,ai,t] = preparets(net,X,{},T); % Setup Division of Data for Training, Validation, Testing net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; % Train the Network [net,tr] = train(net,x,t,xi,ai); % Test the Network y = net(x,xi,ai); e = gsubtract(t,y); performance = perform(net,t,y) % View the Network view(net) % Plots % Uncomment these lines to enable various plots. %figure, plotperform(tr) %figure, plottrainstate(tr) %figure, ploterrhist(e) %figure, plotregression(t,y) %figure, plotresponse(t,y) %figure, ploterrcorr(e) %figure, plotinerrcorr(x,e) % Closed Loop Network % Use this network to do multi-step prediction. % The function CLOSELOOP replaces the feedback input with a direct % connection from the outout layer. netc = closeloop(net); netc.name = [net.name ' - Closed Loop']; view(netc) [xc,xic,aic,tc] = preparets(netc,X,{},T); yc = netc(xc,xic,aic); closedLoopPerformance = perform(net,tc,yc) % Step-Ahead Prediction Network % For some applications it helps to get the prediction a timestep early. % The original network returns predicted y(t+1) at the same time it is % given y(t+1). For some applications such as decision making, it would % help to have predicted y(t+1) once y(t) is available, but before the % actual y(t+1) occurs. The network can be made to return its output a % timestep early by removing one delay so that its minimal tap delay is now % 0 instead of 1. The new network returns the same outputs as the original % network, but outputs are shifted left one timestep. nets = removedelay(net); nets.name = [net.name ' - Predict One Step Ahead']; view(nets) [xs,xis,ais,ts] = preparets(nets,X,{},T); ys = nets(xs,xis,ais); stepAheadPerformance = perform(nets,ts,ys) %% Prediction % Creates a prediction using an already trained closed NARX %X = tonndata(InputT5,false,false); %T = tonndata(OutputT5,false,false); %% Drive Cycle 1 X2=TestInput2; T2=TestOutput2; [x,xi,ai,t] = preparets(netc,X2,{},T2); yc2 = netc(x,xi,ai); yc2=yc2'; a=(cell2mat(T2))'; b=cell2mat(yc2); error2 = immse(a(3:end),b) clear a b %% Drive Cycle 2 X11=TestInput11; T11=TestOutput11; [x,xi,ai,t] = preparets(netc,X11,{},T11); yc11 = netc(x,xi,ai); yc11=yc11'; a=(cell2mat(T11))'; b=cell2mat(yc11); error11 = immse(a(3:end),b) clear a b %% Drive Cycle 3 X19=TestInput19; T19=TestOutput19; [x,xi,ai,t] = preparets(netc,X19,{},T19); yc19 = netc(x,xi,ai); yc19=yc19'; a=(cell2mat(T19))'; b=cell2mat(yc19); error19 = immse(a(3:end),b) clear a b T2=T2'; T11=T11'; T19=T19'; %clearvars -except yc2 yc11 yc19 T2 T11 T19 error=error2+error11+error19; if error<=max max=error; save('netc','netc') save('net','net') end end