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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% 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
SEE COMPLETE ANSWER CLICK THE LINK
