Hi,
% Solve an Autoregression Problem with External Input with a NARX Neural Network % Script generated by Neural Time Series app % Created 16-Jul-2015 12:18:44 % % This script assumes these variables are defined: % % inputs - input time series. % targets - feedback time series. X = tonndata(inputs,true,false); T = tonndata(targets,true,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 = 8; 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 = 'divideblock'; 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'}; % 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) % 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)
[v1,v2,v3], [v2,v3,v4],[v3,v4,v5],...
and
v4,v5,v6,...
respectively.
Because of how I set up my network, I expected my x and t variables to look like
[inputs at time t]| [v2,v3,v4], [v3,v4,v5], ... target from t-1 | v4 , v5 , ...
and
v5, v6, ...
respectively (which clearly isn't perfect as I already have my target fedback from time t-1 in my input at time t but I know how to fix that) but instead I get something even weirder and this time unexpected:
[v2,v3,v4], [v3,v4,v5], ... v5 , v6 , ...
and
v5, v6, ...
ANSWER
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close all, clear all, clc [ X0 T ] = simplenarx_dataset; x0 = cell2mat( X0 ); X = con2seq( [ x0; x0; x0 ] ); x = cell2mat(X); whos % Name Size Bytes Class % T 1x100 12000 cell % X 1x100 13600 cell % X0 1x100 12000 cell % x 3x100 2400 double % x0 1x100 800 double ID = 1, FD = 1, H = 8 neto = nar
% Check: 0.7*0.12489+0.15*(0.18163+0.23894) = 0.15051
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