if true
endHello,
I have recently started working on neural network with MATLAB. I am dealing with gas turbine dynamic model and facing problems in determining optimal value of input and feedback delays. I have gone through similar posts that suggests to look for target auto-correlation and input target cross-correlation but I am unable to read the graph to determine delays.I am attaching the graph for target auto-correlation and input target cross-correlation along with the code.(Code is taken from one of the example given for simplenarx data)
close all, clear all, clc,
tic
plt=0;
load GT_complete.dat
FuelFlowLhr = GT_complete(:,12);
N1RPM = GT_complete(:,15);
x_1 = FuelFlowLhr(150:800,:);
t_1 = N1RPM(150:800,:);
X1 = x_1';
T1 = t_1';
X = con2seq(X1);
T = con2seq(T1);
x = cell2mat(X);
t = cell2mat(T);
[ I N ] = size(x);
[ O N ] = size(t);
Ntrn = N-2*round(0.15*N)
trnind = 1:Ntrn;
Ttrn = T(trnind);
Ntrneq = prod(size(Ttrn))
MSE00 = var(t',1)
zx = zscore(x, 1);
zt = zscore(t, 1);
zxtrn = zscore(x(trnind), 1);
zttrn = zscore(t(trnind), 1);
plt = plt+1,figure(plt);
subplot(221)
plot(x)
title('Fuelflow INPUT SERIES')
subplot(222)
plot(zx)
title('STANDARDIZED INPUT SERIES')
subplot(223)
plot(t)
title('RPM OUTPUT SERIES')
subplot(224)
plot(zt)
title('STANDARDIZED OUTPUT SERIES')
rng('default')
L = floor(0.95*(2*N-1))
for i = 1:1000
n = zscore(randn(1,N),1);
autocorrn = nncorr( n,n, N-1, 'biased');
sortabsautocorrn = sort(abs(autocorrn));
thresh95T(i) = sortabsautocorrn(L);
nx = zscore(randn(1,N),1);
nt = zscore(randn(1,N),1);
autocorrnIT = nncorr( nx,nt, N-1, 'biased');
sortabsautocorrnIT = sort(abs(autocorrnIT));
thresh95IT(i) = sortabsautocorrnIT(L);
end
sigTthresh95 = median(thresh95T)
meanTthresh95 = mean(thresh95T)
minTthresh95 = min(thresh95T)
medTthresh95 = median(thresh95T)
stdTthresh95 = std(thresh95T)
maxTthresh95 = max(thresh95T)
sigITthresh95 = median(thresh95IT)
meanITthresh95 = mean(thresh95IT)
mintIThresh95 = min(thresh95IT)
medtIThresh95 = median(thresh95IT)
stdtIThresh95 = std(thresh95IT)
maxtIThresh95 = max(thresh95IT)
autocorrt = nncorr(zttrn,zttrn,Ntrn-1,'biased');
sigflag95 = -1+ find(abs(autocorrt(Ntrn:2*Ntrn-1))>=sigTthresh95);
sigflag95(sigflag95==0)=[];
plt = plt+1, figure(plt);
hold on
plot(0:Ntrn-1, -sigTthresh95*ones(1,Ntrn),'b--')
plot(0:Ntrn-1, zeros(1,Ntrn),'k')
plot(0:Ntrn-1, sigTthresh95*ones(1,Ntrn),'b--')
plot(0:Ntrn-1, autocorrt(Ntrn:2*Ntrn-1))
plot(sigflag95,autocorrt(Ntrn+sigflag95),'ro')
title('SIGNIFICANT TARGET AUTOCORRELATIONS (FD)')
crosscorrxt = nncorr(zxtrn,zttrn,Ntrn-1,'biased');
sigilag95 = -1 + find(abs(crosscorrxt(Ntrn:2*Ntrn-1))>=sigITthresh95)
plt = plt+1, figure(plt);
hold on
plot(0:Ntrn-1, -sigITthresh95*ones(1,Ntrn),'b--')
plot(0:Ntrn-1, zeros(1,Ntrn),'k')
plot(0:Ntrn-1, sigITthresh95*ones(1,Ntrn),'b--')
plot(0:Ntrn-1, crosscorrxt(Ntrn:2*Ntrn-1))
plot(sigilag95,crosscorrxt(Ntrn+sigilag95),'ro')
title('SIGNIFICANT INPUT-TARGET CROSSCORRELATIONS (ID)')
