Clear Filters
Clear Filters

SUGGESTIONS TO IMPROVE OPEN LOOP NARNET MODEL PERFORMANCE FOR MULTISTEP AHEAD PREDICTIONS (WIND SPEED DATASET)

1 view (last 30 days)
Saira AlZadjali
Saira AlZadjali on 18 Oct 2018
Dears, I am Student working on Project (to predict wind Speed), and I am Student of MATHWORKS too learning how to build my model it have been almost a year i am reading and fixing. I got the steps and idea/suggestions specially from Sir Greg and by following the steps and idea i can get good results for DataSet in MATLAB, but for my DataSet its not working. let me explain... the below steps are explained by Sir Greg in many Posts, so i started to follow the steps...
% 1. Use NNCORR to determine the statistically significant input and/or % feedback lags to be stored in row vectors ID and/or FD with lengths % LID and LFD, respectively. % SZ: Since I am using NARnet, I will only find FD.
% 2. Use an outer loop search over a range of ~10 values (for number of % hidden nodes h = Hmin:dH:Hmax )
% 3. An inner loop search over Ntrials ~10 RNG states (i=1:Ntrials) that % determine random initial weights and random trn/val/tst data division.
%4. MSE goal ~ 0.01*mean(var(target',1)) (Models ~99% of target variance) % 5. MinGrad = MSEgoal/100
all these steps are followed, with data in MATLAB "simplenar_dataset" as below, .. everything went smoothly. but when i used my dataset, i am not getting FD from NNCORR steps (I am getting a huge number like ~8400, which is too much to apply my computer get stuck and cant continue with the selections). Please Advise what I need to try else to make my MODEL to predict multi step ahead with better accuracy as its working with Matlab dataset.
% 1. Use NNCORR to determine the statistically significant input and/or
% feedback lags to be stored in row vectors ID and/or FD with lengths
% LID and LFD, respectively. For NARnet you need FD only
% NNCORR
close all, clear all, clc , plt = 0;
S = simplenar_dataset;
T=S(1:80); % remaining 20 for prediction and test
t = cell2mat(T) ;
[ O N ] = size(t) % [ 1 80]
zt = zscore(t,1); tzt = [ t ; zt ];
meantzt = mean( tzt' )' %[ 0.7233 ;-0.0000]
vartzt1 = var( tzt', 1)' % [0.0635 ; 1 ]
minmaxtzt = minmax(tzt)
% minmaxtzt =
% 0.1622 0.9999
% -2.2273 1.0981
Ntst = round(0.15*N), Nval = Ntst % 12, 12
Ntrn = N-Nval-Ntst % 56
trnind = 1:Ntrn; % 1 : 56
valind = Ntrn+1:Ntrn+Nval; % 57 : 68
tstind = Ntrn+Nval+1:N; % 69 : 80
ttrn = t(trnind); tval = t(valind);ttst = t(tstind);
plt = plt+1, figure(plt), hold on
plot( trnind, ttrn,'b' )
plot( valind, tval,'g' )
plot( tstind, ttst, 'r' )
legend('TRAIN','VAL','TEST')
title( ' DATASET TIMESERIES ' )
rng('default');
Ltrn = floor(0.95*(2*Ntrn-1)) % 105
imax = 100
for i = 1:imax
n = randn(1,Ntrn);
autocorrn = nncorr( n,n, Ntrn-1, 'biased');
sortabsautocorrn = sort(abs(autocorrn));
thresh95(i) = sortabsautocorrn(Ltrn);
end
% imax = 100
minthresh95 = min(thresh95) % 0.0983
medthresh95 = median(thresh95) % 0.1841
meanthresh95 = mean(thresh95) % 0.1950
stdthresh95 = std(thresh95) % 0.0515
maxthresh95 = max(thresh95) % 0.3360
sigthresh95 = meanthresh95 % 0.1950
zttrn = zscore(ttrn',1)';
autocorrttrn = nncorr( zttrn, zttrn, Ntrn-1, 'biased' );
siglag95 = find(abs(autocorrttrn(Ntrn+1:2*Ntrn-1)) ...
>= sigthresh95);
Nsiglag95 = length(siglag95) % 30 ~ 0.54*Ntrn
d = min(find(diff(siglag95)>1)) % 5
proof = siglag95(d-1:d+1) % 4 5 7
plt = plt+1; figure(plt); hold on
plot(0:Ntrn-1, -sigthresh95*ones(1,Ntrn),'b--' );
plot(0:Ntrn-1, zeros(1,Ntrn),'k');
plot(0:Ntrn-1, sigthresh95*ones(1,Ntrn),'b--' );
plot(0:Ntrn-1, autocorrttrn(Ntrn:2*Ntrn-1));
plot( siglag95, autocorrttrn(Ntrn + siglag95), 'ro' );
title('SIGNIFICANT AUTOCORRELATIONS VALUES')
to = t(d+1:N);
No = N-d % 75
Notst = round(0.15*No) % 11
Noval = Notst % 11
Notrn = No-Noval-Notst % 53
% --------------------------------------------------------------------- %
% 2. Use an outer loop search over a range of ~10 values (for number of
% hidden nodes h = Hmin:dH:Hmax )
% 3. An inner loop search over Ntrials ~10 RNG states (i=1:Ntrials) that
% determine random initial weights and random trn/val/tst data division.
% -----------------------------------------------------------------------%
% finding the best suited FD and H
H = 10 % Default
neto = narnet(1:d,H); % d=5
neto.divideFcn = 'divideblock';
[ Xo, Xoi, Aoi, To ] = preparets(neto,{},{},T);
to = cell2mat(To); varto1 = var(to,1) % 0.0628
Hub = (Notrn*O-O)/(d+O+1) % 7.4286
Hmin = 1, dH = 1, Hmax = 7
Ntrials = 5
rng('default')
j = 0
for h = Hmin:dH:Hmax
j = j+1
neto.layers{1}.dimensions = h;
for i = 1:Ntrials
neto = configure(neto,Xo,To);
[neto tro Yo Eo Xof Aof ]=train(neto,Xo,To,Xoi,Aoi);
%[Yo Xof Aof =net(Xo,Xoi,Aoi);Eo=gsubtract(To,Yo);
NMSEo(i,j) = mse(Eo)/varto1;
NMSEtrno(i,j) = tro.best_perf/varto1;
NMSEvalo(i,j) = tro.best_vperf/varto1;
NMSEtsto(i,j) = tro.best_tperf/varto1;
end
end
plotresponse(To,Yo)
NumH = Hmin:dH:Hmax
Rsqo = 1-NMSEo
Rsqtrno = 1-NMSEtrno
Rsqvalo = 1-NMSEvalo
Rsqtsto = 1-NMSEtsto
% NumH =
%
% 1 2 3 4 5 6 7
%
%
% Rsqo =
%
% 0.9981 0.9992 0.9990 1.0000 1.0000 1.0000 1.0000
% 0.9981 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
% 0.9981 1.0000 0.9988 1.0000 1.0000 1.0000 1.0000
% 0.9980 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
% 0.9981 0.9994 1.0000 1.0000 1.0000 1.0000 1.0000
%
%
% Rsqtrno =
%
% 0.9979 0.9991 0.9988 1.0000 1.0000 1.0000 1.0000
% 0.9979 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
% 0.9979 1.0000 0.9986 1.0000 1.0000 1.0000 1.0000
% 0.9979 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
% 0.9979 0.9993 1.0000 1.0000 1.0000 1.0000 1.0000
%
%
% Rsqvalo =
%
% 0.9986 0.9999 0.9991 1.0000 1.0000 1.0000 1.0000
% 0.9986 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
% 0.9986 1.0000 0.9989 1.0000 1.0000 1.0000 1.0000
% 0.9986 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
% 0.9986 0.9998 1.0000 1.0000 1.0000 1.0000 1.0000
%
%
% Rsqtsto =
%
% 0.9984 0.9992 0.9996 1.0000 1.0000 1.0000 1.0000
% 0.9984 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
% 0.9984 1.0000 0.9993 1.0000 1.0000 1.0000 1.0000
% 0.9984 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
% 0.9984 0.9993 1.0000 1.0000 1.0000 1.0000 1.0000
%-------------------------------------------------------------------%
% From above FD is obtained to be 5 and H neurons, actually we
% should select the min H which gives Rsqtsto >0.995, in this case H=1 is
% suitable.
%------------------------------------------------------------------%
% now using the FD & H to train the network and predict multi steps ahead
% 4. MSE goal ~ 0.01*mean(var(target',1)) (Models ~99% of target variance)
% 5. MinGrad = MSEgoal/100
%
% MSEgoal = ~ 6.2800e-04
% MinGrad = ~6.2800e-06
close all, clear all, clc , plt = 0;
S = simplenar_dataset;
T= S(1:80);
[ I N ] = size(T) % [ 1 100 ]
FD = 1:5; H = 1; % obtained from NNCORR
neto = narnet( FD, H );
neto.divideFcn = 'divideblock';
%view(neto)
[ Xo, Xoi, Aoi, To ] = preparets( neto, {}, {}, T );
to = cell2mat(To); varto1 = var(to,1)
neto.trainParam.epochs = 1000; % DEfault
% MSEgoal = ~ 6.2800e-04
% MinGrad = ~6.2800e-06
neto.trainParam.goal= 6.2800e-04 ;
neto.trainParam.min_grad=6.2800e-06 ;
rng( 'default' )
[ neto, tro, Yo, Eo, Aof, Xof ] = train( neto, Xo, To, Xoi, Aoi );
%[ Yo Xof Aof ] = net( Xo, Xoi, Aoi )
%Eo = gsubtract( To, Yo )
%view( neto )
NMSEo = mse( Eo ) /varto1 % 0.0024
yo = cell2mat( Yo );
% plot( d+1:N, yo, 'ro', 'LineWidth', 2 )
% axis( [ 0 100 0 1.3 ] )
% legend( 'TARGET', 'OUTPUT' )
% title( 'OPENLOOP NARNET RESULTS' )
% now converted to closed loop to do multi step predictions
[ netc, Xci, Aci ] = closeloop( neto, Xoi, Aoi );
%view( netc )
[ Xc, Xci, Aci, Tc ] = preparets( netc, {}, {}, T );
isequal( Tc, To ) , tc = to ; % 1
[ Yc, Xcf, Acf ] = netc( Xc, Xci, Aci );
Ec = gsubtract( Tc, Yc );
yc = cell2mat( Yc );
NMSEc = mse(Ec) /var(tc,1) %0.0245
Tp= S(81:100)
m=1;
n=6;
Tf= Tp (m:n);
[ Xc1, Xci1, Aci1, Tp1 ] = preparets( netc, {}, {}, Tf );
[ Yc1, Xcf, Acf ] = netc( Xc1, Xci1, Aci1 );
Xc2 = cell(1,10);
[ Yc2, Xcf2, Acf2 ] = netc( Xc2,Xcf, Acf );
yc2 = cell2mat(Yc2);
Tpp =cell2mat(Tp);
plt = plt+1; figure(plt), hold on
plot( yc2,'b', 'LineWidth', 2 )
plot( Tpp(7:16), 'r', 'LineWidth', 2 )
legend( 'OUTPUT','TARGET')
title( 'PREDICTION RESULTS' )
% >> yc2
% yc2 =
% 0.9985 0.4726 0.6192 0.9853 0.8055 0.3500 0.9204 0.9794 0.3134 0.7738
%>> Tpp(7:16)
%ans =
% 0.9599 0.5351 0.6340 0.9911 0.7724 0.3774 0.9389 0.9925 0.2680 0.7762
%>> Ep = gsubtract( Tpp(7:16), yc2 )
%Ep =
% -0.0386 0.0625 0.0148 0.0058 -0.0331 0.0274 0.0186 0.0131 -0.0454 0.0024
%>> mse (Ec)
%ans =
% 0.0010
All Above were the full complete steps to Design a NARnet Model, For each Data different FD, H , MSE goal, MinGrad and for sure Training Algorithm and the data division tech. For my data I tried to change all these parameters and do test and trials for different FD & H (as NNCORR not geving good results too), But Iam not getting the desired NMSEo, to continue ... :( Please Help...
below the results for my data, i randomly selected FD=6 and kept H=10 as default..
close all, clear all, clc , plt = 0;
data_1= xlsread('G:\SQU2\Solar_Wind_Project\MAtlabCodes\data_Nov13-Oct15');
T=data_1(1:92394,2);
T=con2seq(T');
[ I N ] = size(T) % [ 1 100 ]
d=6; FD = 1:d; H = 10; % obtained from NNCORR
neto = narnet( FD, H );
neto.divideFcn = 'divideblock';
%view(neto)
[ Xo, Xoi, Aoi, To ] = preparets( neto, {}, {}, T );
to = cell2mat(To); varto1 = var(to,1) % 12.7740
neto.trainParam.epochs = 1000; % DEfault
% MSEgoal = ~ 0.1277
% MinGrad = ~0.0013
neto.trainParam.goal= 0.1277 ;
neto.trainParam.min_grad=0.0013 ;
rng( 'default' )
[ neto, tro, Yo, Eo, Aof, Xof ] = train( neto, Xo, To, Xoi, Aoi );
%[ Yo Xof Aof ] = net( Xo, Xoi, Aoi )
%Eo = gsubtract( To, Yo )
%view( neto )
NMSEo = mse( Eo ) /varto1 % 0.0291
% now converted to closed loop to do multi step predictions
[ netc, Xci, Aci ] = closeloop( neto, Xoi, Aoi );
%view( netc )
[ Xc, Xci, Aci, Tc ] = preparets( netc, {}, {}, T );
%[ netc, trc, Yc, Ec, Acf, Xcf ] = train( netc, Xc, Tc, Xci, Aci );
isequal( Tc, To ) , tc = to ; % 1
[ Yc, Xcf, Acf ] = netc( Xc, Xci, Aci );
Ec = gsubtract( Tc, Yc );
yc = cell2mat( Yc );
NMSEc = mse(Ec) /var(tc,1) %1.9321

Sign in to answer this question.

Answers (0)

Categories

Find more on Define Shallow Neural Network Architectures in Help Center and File Exchange

Tags

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!