I have some questions with regards to training a closeloop of a narxnet from using the weights from open-loop. Following an answer greg gave on 4th november 2016 by using netc = closeloop(neto); netc = train(netc,X,Xoi,Aoi); where do i exactly place it in my code so as to avoid error because i have been trying it and keep on running into error when i place it at the closeloop session of my code. The code is given below. Any suggestions concerning my code are welcome so as to enable me do a good prediction. Thank you in advance. % Solve an Autoregression Problem with External Input with a NARX Neural Network % Script generated by Neural Time Series app % Created 04-Jan-2017 05:53:56 % % This script assumes these variables are defined: % % normtrip - input time series. % normw - feedback time series.
X = tonndata(normtrip,false,false); T = tonndata(normw,false,false);
% 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 = 'trainlm'; % Levenberg-Marquardt backpropagation.
% Create a Nonlinear Autoregressive Network with External Input inputDelays = 1:1; feedbackDelays = 1:1; hiddenLayerSize = 10; net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize,'open',trainFcn);
% Choose Input and Feedback Pre/Post-Processing Functions % Settings for feedback input are automatically applied to feedback output % For a list of all processing functions type: help nnprocess % Customize input parameters at: net.inputs{i}.processParam % Customize output parameters at: net.outputs{i}.processParam net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'}; net.inputs{2}.processFcns = {'removeconstantrows','mapminmax'};
% 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 % For a list of all data division functions type: help nndivide net.divideFcn = 'dividerand'; % Divide data randomly net.divideMode = 'time'; % Divide up every sample net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100;
% Choose a Performance Function % For a list of all performance functions type: help nnperformance net.performFcn = 'mse'; % Mean Squared Error
% Choose Plot Functions % For a list of all plot functions type: help nnplot net.plotFcns = {'plotperform','plottrainstate', 'ploterrhist', ... 'plotregression', 'plotresponse', 'ploterrcorr', 'plotinerrcorr'}; rng('default') % 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) B=cell2mat(y); A=cell2mat(t); x=(1:1:67); figure plot(x,A,x,B) % Recalculate Training, Validation and Test Performance trainTargets = gmultiply(t,tr.trainMask); valTargets = gmultiply(t,tr.valMask); testTargets = gmultiply(t,tr.testMask); trainPerformance = perform(net,trainTargets,y) valPerformance = perform(net,valTargets,y) testPerformance = perform(net,testTargets,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']; %will deal with later view(netc); [xc,xic,aic,tc] = preparets(netc,X,{},T); %netc = train(netc,x,xi,ai); yc = netc(xc,xic,aic); closedLoopPerformance = perform(net,tc,yc);
% Multi-step Prediction % Sometimes it is useful to simulate a network in open-loop form for as % long as there is known output data, and then switch to closed-loop form % to perform multistep prediction while providing only the external input. % Here all but 5 timesteps of the input series and target series are used % to simulate the network in open-loop form, taking advantage of the higher % accuracy that providing the target series produces: numTimesteps = size(x,2); knownOutputTimesteps = 1:(numTimesteps-5); predictOutputTimesteps = (numTimesteps-4):numTimesteps; X1 = X(:,knownOutputTimesteps); T1 = T(:,knownOutputTimesteps); [x1,xio,aio] = preparets(net,X1,{},T1); [y1,xfo,afo] = net(x1,xio,aio); % Next the the network and its final states will be converted to % closed-loop form to make five predictions with only the five inputs % provided. x2 = X(1,predictOutputTimesteps); [netc,xic,aic] = closeloop(net,xfo,afo); [y2,xfc,afc] = netc(x2,xic,aic); multiStepPerformance = perform(net,T(1,predictOutputTimesteps),y2) % Alternate predictions can be made for different values of x2, or further % predictions can be made by continuing simulation with additional external % inputs and the last closed-loop states xfc and afc.
% 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)
% Deployment % Change the (false) values to (true) to enable the following code blocks. % See the help for each generation function for more information. if (false) % Generate MATLAB function for neural network for application % deployment in MATLAB scripts or with MATLAB Compiler and Builder % tools, or simply to examine the calculations your trained neural % network performs. genFunction(net,'myNeuralNetworkFunction'); y = myNeuralNetworkFunction(x,xi,ai); end if (false) % Generate a matrix-only MATLAB function for neural network code % generation with MATLAB Coder tools. genFunction(net,'myNeuralNetworkFunction','MatrixOnly','yes'); x1 = cell2mat(x(1,:)); x2 = cell2mat(x(2,:)); xi1 = cell2mat(xi(1,:)); xi2 = cell2mat(xi(2,:)); y = myNeuralNetworkFunction(x1,x2,xi1,xi2); end if (false) % Generate a Simulink diagram for simulation or deployment with. % Simulink Coder tools. gensim(net); end %B=cell2mat(yc); %A=cell2mat(t); %x=(1:1:67); %figure %plot(x,A,x,B)
