As predicted delayed outputs settle in NarX

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simran_k - 2021-07-20T11:15:48+00:00
Question: As predicted delayed outputs settle in NarX

I applied my code to data simplenarx_dataset. To do this I performed the following steps: 1 - I have done autocorrelation and cross correlation peaks to see that gives us more information. ID = 1, FD = 1 2 - I have found H, where H = 5 3 - I have created the network and have evaluated the details. Although the purpose of this post is not to evaluate the details but understand why you see a delayed response when performing closeloop, but public details and code In case of emergency there is some other error: My code is as follows(I used 80 data for training the network and 20 to check with closeloop): p=p'; t=t'; p1=p(1:1,1:80); p2=p(1:1,81:end); t1=t(1,1:80); t2=t(1,81:end); inputSeries = tonndata(p1,true,false); targetSeries = tonndata(t1,true,false); inputDelays = 1:1; feedbackDelays = 1:1; hiddenLayerSize = 5; net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize); [inputs,inputStates,layerStates,targets] = preparets(net,inputSeries,{},targetSeries); net.divideFcn='divideblock'; net.divideParam.trainRatio=0.70; net.divideParam.valRatio=0.15; net.divideParam.testRatio=0.15; [I N]=size(p1); [O N]=size(t1); N=N-1; Neq=N*O; ID=1; FD=1; Nw = (ID*I+FD*O+1)*hiddenLayerSize+(hiddenLayerSize+1)*O; Ntrneq = N -2*round(0.15*N); Ndof=Ntrneq-Nw; ttotal=t1(1,1:N); MSE00=mean(var(ttotal,1)); MSE00a=mean(var(ttotal,0)); t3=t(1,1:N); [trainInd,valInd,testInd] = divideblock(t3,0.7,0.15,0.15); MSEtrn00=mean(var(trainInd,1)); MSEtrn00a=mean(var(trainInd,0)); MSEval00=mean(var(valInd,1)); MSEtst00=mean(var(testInd,1)); net.trainParam.goal = 0.01*Ndof*MSEtrn00a/Ntrneq; [net,tr,Ys,Es,Xf,Af] = train(net,inputs,targets,inputStates,layerStates); outputs = net(inputs,inputStates,layerStates); errors = gsubtract(targets,outputs); MSE = perform(net,targets,outputs); MSEa=Neq*MSE/(Neq-Nw); R2=1-MSE/MSE00; R2a=1-MSEa/MSE00a; MSEtrn=tr.perf(end); MSEval=tr.vperf(end); MSEtst=tr.tperf(end); R2trn=1-MSEtrn/MSEtrn00; R2trna=1-MSEtrn/MSEtrn00a; R2val=1-MSEval/MSEval00; R2tst=1-MSEtst/MSEtst00; and my results are: ID=1 FD=1 H=5 N=79 Ndof=34 Neq=79 Ntrneq=55 Nw=21 O=1 I=1 R2=0.8036 R2a=0.7347 R2trn=0.8763 R2trna=0.8786 R2val=0.7862 R2tst=0.7541 As I mentioned earlier, I will not focus much on the accuracy in the answer but later will. The code I applied for closeloop was: netc = closeloop(net); netc.name = [net.name ' - Closed Loop']; view(netc) NumberOfPredictions = 15; s=cell2mat(inputSeries); t4=cell2mat(targetSeries); a=s(1:1,79:80); b=p2(1:1,1:15); newInputSeries=[a b]; c=t4(1,80); d=nan(1,16); newTargetSet=[c d]; newInputSeries=tonndata(newInputSeries,true,false); newTargetSet=tonndata(newTargetSet,true,false); [xc,xic,aic,tc] = preparets(netc,newInputSeries,{},newTargetSet); yPredicted = sim(netc,xc,xic,aic); w=cell2mat(yPredicted); plot(cell2mat(yPredicted),'DisplayName','cell2mat(yPredicted)','YdataS ource','cell2mat(yPredicted)');figure(gcf) plot(t2,'r','DisplayName','targetsComprobacion') hold on plot(w,'b','DisplayName','salidasIteradas') title({'ITERACCIONES'}) legend('show') hold off and the result was the chart that you have indicated the link below where you will see it: http://www.subirimagenes.com/otros-simenarx-8376264.html In this picture we see the blue line (line outputs predicted) lags behind the red line (real targets). I'd like to know how I can do to get that blue line is in front of the red line, that is one step get out early. As I said, in this post I want to focus on why this happens and how I can fix it.

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    • Expert Answer

    Profile picture of Prashant Kumar Prashant Kumar answered . 2025-11-20

    % 1. Selected ending semicolons can be removed to aid debugging
     
     
     [P, T ] = simplenarx_dataset;
 whos
 p= cell2mat(P);
 t = cell2mat(T);

 ID = 1:1
 FD = 1:1
 H = 5

 NID= length(ID)
 NFD=length(FD)
 Nw = (NID*I+NFD*O+1)*H+(H+1)*O
    % 2. Use NID and NFD for Nw in case delays are not single
    % 3. No need to use tonndata because the simplenarx_data set is instantly ready for preparets.
    % 4. No need for (p1,t1) and (p2,t2). Delete both.
    % 5. Input delays are suboptimal. Did you try to find the significant lags of the target/input cross-correlation function?
    % 6. Feedback delays are suboptimal. Did you try to find the significant lags of the target autocorrelation function?
    % 7. H is suboptimal. Was it chosen using the suboptimal delays? If so, please explain how.
     rng(0)
 net = narxnet(ID,FD,H);
 [inputs,inputStates,layerStates,targets] = preparets(net,P,{},T);
 whos P T inputs inputStates layerStates targets
    %8. N=N-1: DELETE. NOT A GOOD IDEA TO USE A VARIABLE OR PARAMETER NAME ON BOTH SIDES OF AN EQUATION. BESIDES, PREPARETS OUTPUTS THE CORRECT DIMENSIONS
     
     
     [ I N ] = size(inputs)
 [ O N ] = size(targets)
    % 9. No need for ttotal it should be the same as targets. % No need for Neq, MSE00,MSE00a and t4. Delete
     
     
     net.divideFcn='divideblock';
 [trainInd,valInd,testInd] = divideblock(N,0.7,0.15,0.15); 
 ttrn = targets(trainInd);
 tval = targets(valInd);
 ttest = targets(testInd);
 Ntst = length(ttrn) 
 Nval = length(valInd)
 Ntst = length(testInd)
 Ntrneq = prod(size(ttrn))     % Ntrn*O
 Ndof = Ntrneq-Nw
    %Naive Constant Output Model
     
     
     ytrn00= mean(ttrn,2);     
 Nw00 = size(ytrn00,2)
 Ndof00 = Ntrneq-Nw00
 MSEtrn00 = sse(ttrn-ytn000)/Ntrneq
 MSEtrn00=mean(var(ttrn,1))
 MSEtrn00a = sse(ttrn-ytrn00)/Ndof00
 MSEtrn00a=mean(var(ttrn,0))
    %9. MSEval00 and MSEtst00 should be obtained from the Naive constant output model output
     
     
     MSEval00 = mse(tval-ytrn00)
 MSEtst00 = mse(tttst-ytrn00)

 net.trainParam.goal = 0.01*Ndof*MSEtrn00a/Ntrneq;  % R2trna >= 0.99
 rng(0)
 [net,tr,Ys,Es,Xf,Af] = train(net,inputs,targets,inputStates,layerStates);

 outputs = net(inputs,inputStates,layerStates);

 errors = gsubtract(targets,outputs);

 MSE = perform(net,targets,outputs);

 MSEa=Neq*MSE/(Neq-Nw)

 R2=1-MSE/MSE00

 R2a=1-MSEa/MSE00a

    % 10. The DOF "a"djustment is only applied to the training data

    % 11. Can delete the last 6 equations that refer to all of the data instead of the trn/val/tst division.

     MSEtrn=tr.perf(end)
 MSEtrna = Ntrneq*MSEtrn/Ndof
 MSEval=tr.vperf(end)
 MSEtst=tr.tperf(end)
    % 12.Using "end" is only valid if training converges because of tr.min_grad (not valstop ). Better to use "tr.best_epoch".
     
     
     R2trn=1-MSEtrn/MSEtrn00
 R2trna=1-MSEtrna/MSEtrn00a
    %13 Original MSEtrna misprint.
     
     
     R2val=1-MSEval/MSEval00
 R2tst=1-MSEtst/MSEtst00
    and my results are:
    % 14. Unable to compare results because you did not intialize the RNG before the first call of the RNG in the net creation command net = ... where H =5 random weights were assigned to the input bias. I will use rng(0) before the net creation.


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