NARX OPTIMUM HIDDEN NODES NUMBER
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I used the code below to try to get the optimum number of hidden nodes for a narx network....I would like any suggestions concerning if I am implementing the code correctly. THANKS
close all,clear all, clc, plt=0;
tic
load 'ProjectD'
X = GasProduced; %[1x1500] cell
T = OilRate; %[1x1500] cell
x = cell2mat(X);
t = cell2mat(T);
[ I N ] = size(X); % [ 1 1500]
[ O N ] = size(T);
MSE00 = mean(var(t',1)) % 1.1197e+07
MSE00a = mean(var(t',0)) %1.12041e+07
%Normalization
zx = zscore(cell2mat(X), 1);
zt = zscore(cell2mat(T), 1);
Ntrn = N-2*round(0.15*N)
trnind = 1:Ntrn
Ttrn = T(trnind)
Neq = prod(size(Ttrn)) % 1500
%Significant Lags were determined using the code at :
<http://www.mathworks.com/matlabcentral/newsreader/view_thread/341287#935393>
%sigilag95: [0:517 520 521 524 525 636:1255 1345:1384] %Significant Input Lag
%sigflag95: [0:348 411:1207 1321:1401] %significant Feedback lag
rng('default')
% %
% %
FD = 1:20; %Random Selection of sigflag subset
ID = 1:20; %Random selection of sigilag subset
NFD = length(FD) %
NID = length(ID) %
MXFD = max(FD)
MXID = max(ID)
Ntrneq = prod(size(t))
% Nw = ( NID*I + NFD*O + 1)*H + ( H + 1)*O
Hub = -1+ceil( (Ntrneq-O) / ((NID*I)+(NFD*O)+1))
Hmax = floor(Hub/10) %
Hmax = 2 ==> Nseq >>Nw :
Hmin = 0
dH = 1
Ntrials = 25
j=0
rng(4151941)
for h = Hmin:dH:Hmax
j = j+1
if h == 0
net = narxnet( ID, FD, [] );
Nw = ( NID*I + NFD*O + 1)*O
else
net = narxnet( ID, FD, h );
Nw = ( NID*I + NFD*O + 1)*h + ( h + 1)*O
end
Ndof = Ntrn-Nw
[ Xs Xi Ai Ts ] = preparets(net,X,{},T);
ts = cell2mat(Ts);
xs = cell2mat(Xs);
MSE00s = mean(var(ts',1))
MSE00as = mean(var(ts'))
MSEgoal = 0.01*Ndof*MSE00as/Neq
MinGrad = MSEgoal/10
net.trainParam.goal = MSEgoal;
net.trainParam.min_grad = MinGrad;
net.divideFcn = 'dividetrain';
for i = 1:Ntrials
net = configure(net,Xs,Ts);
[ net tr Ys ] = train(net,Xs,Ts,Xi,Ai);
ys = cell2mat(Ys);
stopcrit{i,j} = tr.stop;
bestepoch(i,j) = tr.best_epoch;
MSE = mse(ts-ys);
MSEa = Neq*MSE/Ndof;
R2(i,j) = 1-MSE/MSE00s;
R2a(i,j) = 1-MSEa/MSE00as;
end
end
stopcrit = stopcrit %Min grad reached (for all).
bestepoch = bestepoch
R2 = R2
R2a = R2a
Totaltime = toc
2 Comments
Greg Heath
on 11 Jun 2015
Run your code on a MATLAB dataset
help nndatasets
doc nndatasets
and I will compare the results with those from my code.
Olumide Oladoyin
on 13 Jun 2015
Accepted Answer
Greg Heath
on 19 Jul 2015
Edited: Walter Roberson
on 19 Jul 2015
0 votes
I get the same results as you.
1. However, there are some code inconsistencies including:
- a. Using Ntrn = 70 with 'dividetrain'
- b. Not taking lags into account when counting equations... the equation count should be based on ttrns.
2. I now find using o (instead of s) and c as subscripts for openloop and closeloop to be more natural.
3. Sorry for not suggesting
- a. less trivial dataset examples like simpleseries and pollution.
- b. Using ID = 0
3. Remember, DIVIDETRAIN is only recommended for estimating the minimum values of ID, FD, and H that will yield acceptable performance. To get reliable estimates of performance on unseen data use data division with Ntrn as small as possible.
Hope this helps.
Greg
1 Comment
Olumide Oladoyin
on 19 Jul 2015
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