MATLAB: Should modify the number of layers to make multi-step closed loop NarX

Deep Learning Toolboxnarxneural

Should modify the number of layers to make multi-step closed loop NarX. How can i have multi-step with closeloop narx?

Best Answer

Maybe this example should help you FRANCISCO. This example predicts 30 time steps ahead for a SINE wave.
    time = 1:10:7200;
    timeh = time(1:end/2);
    timenh = time(end/2 +1 : end);
    input = sind(time(1:end/2));
    toPredict = sind(time(end/2 +1 : end));
    % plot(time(1:end/2),input,'o-g',time(end/2+1:end), toPredict,'+-r')
    % legend('input to neural network','expected output');
    inputSeries = tonndata(timeh,true,false);
    targetSeries = tonndata(input,true,false);
    % Create a Nonlinear Autoregressive Network with External Input
    inputDelays = 1:10;
    feedbackDelays = 1:10;
    hiddenLayerSize = 50;
    net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
    % 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.
    [inputs,inputStates,layerStates,targets] = preparets(net,inputSeries,{},targetSeries);
    % 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,inputs,targets,inputStates,layerStates);
    % Test the Network
    outputs = net(inputs,inputStates,layerStates);
    errors = gsubtract(targets,outputs);
    performance = perform(net,targets,outputs)
    % View the Network
    % view(net)
    % Plots
    % Uncomment these lines to enable various plots.
    %figure, plotperform(tr)
    %figure, plottrainstate(tr)
    %figure, plotregression(targets,outputs)
    %figure, plotresponse(targets,outputs)
    %figure, ploterrcorr(errors)
    %figure, plotinerrcorr(inputs,errors)
    % 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'];
    % Only a certain number of predictions can be made
    %accurately modulate this parameter to check it.
    NumberOfPredictions = 30
    % Creating a new input series which has the time
    % inputs for the second half of the time but also
    % includes last 10 time steps from the previous
    % timeSeries
    newInputSeries = timenh(1:NumberOfPredictions);
    newInputSeries = [cell2mat(inputSeries(end-10:end)  ) (newInputSeries)]
    newInputSeries = num2cell(newInputSeries);
    %Creating a new target with first 10 values which
    %are the expected outputs network and the
    %remaining targets are set to NAN, These values
    %which are set to NAN will be predicted.
    newTargetSet = nan(size(newInputSeries))
    newTargetSet = num2cell(newTargetSet )
    newTargetSet (1:10) = targetSeries(end-9:end)
    [xc,xic,aic,tc] = preparets(netc,newInputSeries,{},newTargetSet);
    yPredicted = sim(netc,xc,xic,aic)
    % timenh = [ timenh timenh(end)+10];
    figure,plot(timeh,cell2mat(targetSeries),'.-',timenh(1:NumberOfPredictions +1),cell2mat(yPredicted),'+-')
    title(['PREDICTION PLOT - NumberOfPredictions =' num2str(NumberOfPredictions)])
    legend('INPUT DATA','PREDICTED DATA')
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