Plz, Edit the NEWFF according to the latest version of MATLAB.
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Anjireddy Thatiparthy
le 6 Août 2013
Commenté : Greg Heath
le 24 Oct 2013
when i simulate the below code it is showing some errors.
like obsolete way of using NEWFF.
what is the new model for it ?
Can some one edit the NEWFF according to the latest version.
- load data.txt
- P = data(1:15,1);
- T = data(16:30,1);
- a = data(31:45,1);
- s = data(46:60,1);
- [py, pys] = mapminmax(P');
- [ay, ays] = mapminmax(a');
- [ty, tys] = mapminmax(T');
- [sy, sys] = mapminmax(s');
- net = newff(minmax(py),[6 1], {'logsig','logsig'}, 'triangdm')
- net.trainParam.epochs = 3000;
- net.trainParam.lr = 0.5;
- net.trainParm.mc = 0.8;
- net = train(net,py,ty);
- y = sim(net,ay);
6 commentaires
Greg Heath
le 13 Août 2013
Modifié(e) : Greg Heath
le 13 Août 2013
1. That is not a clear explanation AND it seems to have little to do with your original post.
2. Why are you posting an equation that
a. is obsolete
b. has inappropriate transfer functions
c. has a misspelled training function (to which you were alerted earlier)
3. If you have 2012a, why are you trying to use the obsolete newff?
4. Now it seems that you might want the simple classifier
output = hardlim(input-5663)
4. Please clarify.
a. Single output y(t) = ( 566x.xx or 0/1?)
b. Corresponding input y( t-d:t-1)
Réponse acceptée
Greg Heath
le 11 Août 2013
This is a Time-Series Problem that can be solved using NARNET with a feedback delay of 15.
help NARNET
doc NARNET
Search NARNET in the NEWSGROUP and ANSWERS
Thank you for formally accepting my answer
Greg
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Plus de réponses (1)
Greg Heath
le 7 Août 2013
if true
% code
end
clear all, clc
[ inputs, targets ] = simplefit_dataset;
P = inputs(1:2:end);
T = targets(1:2:end);
[ I N ] = size(P)
[ O N ] = size(T)
MSE00 = var(T,1) % 8.3328 Reference MSE
Neq = N*O % No. of equations = prod(size(T)
a = inputs(2:2:end);
s = targets(2:2:end);
% Nw = (I+1)*H+(H+1)*O % No. of weights = Nw
{Hub = -1+ceil( (Neq-O)/(I+O+1)) % 15 (Neq >= Nw)
Hmin = 0
dH = 2
Hmax =ceil(Hub/2)
Ntrials = 10
MSEgoal = MSE00/100
MinGrad = MSEgoal/10
rng(0)
j = 0
for h = Hmin:dH:Hmax
j=j+1
if h ==0
net = newff(P,T, []);
else
net=newff(P,T,h);
end
for i = 1:Ntrials
hidden = h
ntrials = i
net.trainParam.goal = MSEgoal;
net.trainParam.min_grad = MinGrad;
[ net tr Y E ]= train(net,P,T);
NMSE(i,j) = mse(E)/MSE00;
end
end
NMSEtst = mse(s-net(a))/var(s,1) %4.0567e-005
H = Hmin:dH:Hmax
NMSE=NMSE
2 commentaires
Greg Heath
le 24 Oct 2013
Sorry I missed your comment. If you have any SPECIFIC questions on the code,
please post.
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