MATLAB: As used in closed loop network NarX reality

Deep Learning Toolboxnarxneural

Thanks Greg for your reply. I think the network is evaluated openloop and I think it gives good results, but for now I do not believe them. My questions are as follows:

1 – I have evaluated the accuracy openloop, how would an in closeloop?. For a first simulation closeloop as inputs I have therefore created a matrix with the last two values InputSeries and say 30 inputs outside the training sample which would have an array of 2 * 32. To create the matrix newsTargets introduce targetSeries the last value of the array and fill with 31 NaN thereby get another matrix 1 * 32. From here I simulate the network and gives me values that putting them together with real targets is a bit displaced. (I would like to send a screenshot of that graph, if you tell me in what way will send it). I think I perform the operation correctly, although I would like to evaluate the accuracy closeloop very strictly.

2 – My second question is because if I try the net for real, that is, suppose we are to March 4, 2013. I by a platform get data (RSI inputs and EMA), whereby I newInputs other matrix in which the last 2 introduce inputSeries values and RSI values and EMA today. (I think I'm doing well so far). To create another array newTarget do with the last value NaN targetSeries and 2 to have the same dimension as newInput (2 * 3) and newTarget (1 * 3) and performed the simulation to obtain the predicted value of the day March 5, 2013 . To continue iterating introduce the value of March 5 and get the value of the March 6, much like the network does NAR?, Is, in preparets command:

[inputs, inputStates, layerStates, targets] = preparets (net, inputSeries, targetSeries);?? and evaluate the network outside the sample.

Basically these are my doubts.

Many thanks Greg

Best Answer

I think the network is evaluated openloop and I think it gives good results,

What are I, N, ID, FD, H, Ntrn, Nval, Ntst, R2trn, R2trna, R2val and R2tst ?

but for now I do not believe them.

Why?
What do you get from the same data using closeloop?

My questions are as follows:

1 - I have evaluated the accuracy openloop, how would an in closeloop?. For a first simulation closeloop as inputs I have therefore created a matrix with the last two values InputSeries and say 30 inputs outside the training sample which would have an array of 2 * 32. To create the matrix newsTargets introduce targetSeries the last value of the array and fill with 31 NaN thereby get another matrix 1 * 32.

I don't understand where this data is coming from.

From here I simulate the network and gives me values that putting them together with real targets is a bit displaced. (I would like to send a screenshot of that graph, if you tell me in what way will send it). I think I perform the operation correctly, although I would like to evaluate the accuracy closeloop very strictly.

There is a way to see plots on ANSWERS. Fnd out how. Right now I need to see your code.

2 - My second question is because if I try the net for real, that is, suppose we are to March 4, 2013. I by a platform get data (RSI inputs and EMA), whereby I newInputs other matrix in which the last 2 introduce inputSeries values and RSI values and EMA today. (I think I'm doing well so far). To create another array newTarget do with the last value NaN targetSeries and 2 to have the same dimension as newInput (2 * 3) and newTarget (1 * 3) and performed the simulation to obtain the predicted value of the day March 5, 2013 . To continue iterating introduce the value of March 5 and get the value of the March 6, much like the network does NAR?, Is, in preparets command:

[inputs, inputStates, layerStates, targets] = preparets (net, inputSeries, targetSeries);?? and evaluate the network outside the sample.

Basically these are my doubts.

 I don't understand. Post the code.

Greg

Related Solutions

MATLAB: As predicted delayed outputs settle in NarX

% 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.

I will do the closeloop part next.

Thank you for formally accepting my answer

Greg

MATLAB: How to predict new values in NARnet after it has been trained

 inPutss = xlsread('Datos','Hoja2'); 
targetSeries = tonndata(inPutss,false,false); 
feedbackDelays = 1:2; 
hiddenLayerSize = 10; 
net = narnet(feedbackDelays,hiddenLayerSize); 
% net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'}; 
[inputs,inputStates,layerStates,targets] = preparets(net,{},{},targetSeries); 
net.divideFcn = 'divideblock'; 
% net.divideMode = 'time'; 
% net.divideParam.trainRatio = 70/100; 
% net.divideParam.valRatio = 15/100; 
% net.divideParam.testRatio = 15/100;
% net.trainFcn = 'trainlm'; 
[net,tr,YY,EE,XFF,AFF] = train(net,inputs,targets,inputStates,layerStates); 
% outputs = net(inputs,inputStates,layerStates); 
% errors = gsubtract(targets,outputs); 
% performance = perform(net,targets,outputs)
performance = perform(net,targets,YY)

'GEH1: NO PROOF THAT THE OL NET IS SUCCESSFUL' ; ' e.g., performance << var(cell2mat(transpose(targets)),1)' ;

 netc = closeloop(net); 
[xc,xic,aic,tc] = preparets(netc,{},{},targetSeries); 
yc = netc(xc,xic,aic); 
perfc = perform(net,tc,yc)

'GEH2: NO PROOF THAT THE CL NET IS SUCCESSFULL';

 nets = removedelay(net);

'GEH3: ERROR: CANNOT HAVE 0 FEEDBACK DELAY';

 [xs,xis,ais,ts] = preparets(nets,{},{},targetSeries); 
ys = nets(xs,xis,ais); 
closedLoopPerformance = perform(net,tc,yc)

'GEH4: TYPO: REPLACE net WITH nets ' ;

Okay now that I have my network trained, I want to use it to predict new values:

 test = xlsread('Datos','Sheet1') 
this is a 1x24 vector 
test2 = tonndata(test,false,false) 
sample1 = net(test2) 
sample2 = netc(test2)
sample3 = nets(test3)

' GEH5: ERROR : REPLACE test3 WITH test2 ';

 My first question is: 
 1- Which one of the three networks that the script created is the one that I should use to
predict next-day values? In other words, which result is better? sample1,sample2 or sample3?

' GEH6: NONE OF THE ABOVE. ';

' a. sample1 is openloop';

' b. sample3 cannot exist with with 0 feedback delay';

' c. sample2 has no specified Xic,Aic.';

 2- How can I add more layers (apart from the hidden ones) and how can I define the number of neurons inside each one of them?

'GEH7: net = narnet( FD, [H1 H2 H3])';

 Thanks to anyone that can answer my questions

Hope this helps.

Thank you for formally accepting my answer

Greg

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Related Solutions

MATLAB: As predicted delayed outputs settle in NarX

MATLAB: How to predict new values in NARnet after it has been trained

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