where do u i place the code netc = closeloop(neto); netc = train(netc,X,Xoi,Aoi); in the code elow to avoid error

Thanks greg for the comments on my earlier question on this matter. I have presented it clearer.

The question is

1. where do i place

netc = closeloop(neto); netc = train(netc,X,Xoi,Aoi);

in order to train closeloop using weights from open-loop. i dont know exactly where to place it the script.

This is the error:

Error using network/train (line 340) Inputs and targets have different numbers of samples.

Error in ab (line 100) (% ab is the matlab script name) netc = train(netc,x,xi,ai)

2. I dont really understand how to go about your third suggestion but i am working on a time series with 68 data points for both input(normtrip) and output(normw) values or is that what i have described in 1.

3. The script is shown below once again: % X and T input and input series and output series variables respectively

X = tonndata(normtrip,false,false); 
T = tonndata(normw,false,false);

% training function

trainFcn = 'trainlm';

% Creating narx network

inputDelays = 1:1; feedbackDelays = 1:1; hiddenLayerSize = 10; net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize,'open',trainFcn);

% Input and Feedback Pre/Post-Processing Functions

net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'}; 
net.inputs{2}.processFcns = {'removeconstantrows','mapminmax'};

% Prepare the Data for Training and Simulation

[x,xi,ai,t] = preparets(net,X,{},T);

% Setup Division of Data for Training, Validation, Testing.

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;

% mse is chosen as performance function

net.performFcn = 'mse'; % Mean Squared Error

% Plot Functions.

net.plotFcns = {'plotperform','plottrainstate', 'ploterrhist', ... 'plotregression', 'plotresponse', 'ploterrcorr', 'plotinerrcorr'};

% initializing weights

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)

% a plot of predicted series y and target(observed)series

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);

% Closed Loop Network

netc = closeloop(net); 
netc.name = [net.name ' - Closed Loop']; 
view(netc); 
[xc,xic,aic,tc] = preparets(netc,X,{},T); 
netc = train(netc,x,xi,ai); (% line 100 where the error occurs where exactly shoult it be placed )
 yc = netc(xc,xic,aic); 
closedLoopPerformance = perform(net,tc,yc);

% Multi-step Prediction, all but 5 timesteps of the input series and output series are used to simulate the network in open loop.

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);

% The network and its final states are 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)

% Step-Ahead Prediction Network

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)

% Changing the (false) values to (true) to enable the following code blocks.

if (false)

genFunction(net,'myNeuralNetworkFunction'); y = myNeuralNetworkFunction(x,xi,ai); end if (false)

      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)
       gensim(net);
  end

% a plot of predicted series from closeloop and target(observed) series.

B=cell2mat(yc); 
A=cell2mat(t); 
x=(1:1:67); 
figure plot(x,A,x,B)

Thank you for the suggestions.

I hope it is now clearer.