Problem in training the feed-forward network.

Using 'simout' options in my simulink model, I saved the input and output data in workspace, which were then called in my program. The input and output are matrix of dimensions "3988*1 double"

My MATLAB program is as follows:

------------

load input

load output

p=input';

t=output';

net=newff(10,30, [10,1], {'tansig', 'purelin'}, 'traingd')

net.trainParam.show=50;

net.trainParam.epochs=1000;

net.trainParam.lr=0.05;

net.trainParam.goal=1e-3;

net1=train(net,p,t)

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Everytime I run this program, the following error is generated.

??? Error using ==> plus

Matrix dimensions must agree.

Error in ==> calcperf2 at 163

        N{i,ts} = N{i,ts} + Z{k};

Error in ==> trainlm at 253

[perf,El,trainV.Y,Ac,N,Zb,Zi,Zl]= calcperf2(net,X,trainV.Pd,trainV.Tl,trainV.Ai,Q,TS);

Error in ==> network.train at 216

[net,tr] = feval(net.trainFcn,net,tr,trainV,valV,testV);

Error in ==> Ann1 at 16

net1=train(net,p,t);

>>

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But I don't understand, How to debug it. Kindly help.

Answer 1

[Greg Heath]

load input

load output

p=input';

t=output';

[ I N } = size(p) % [ 1 3988 ]

[ O N } = size(t) % [ 1 3988 ]

Neq = N*O % No. of training equations

H = 10 % trial No. of hidden nodes for I-H-O node topology

Nw =(I+1)*H+(H+1)*O % 31 = No. of unknown weights to estimate from Neq =3998 eqs.

Ndof = Neq-Nw % No. of estimation DOF (degrees of freedom, (see Wikipedia))

% Since Neq/Nw ~ 128 >> 10, do not need validation data.

Ntrn = N

Ntrneq = Neq

MSEtrn00 = mean(var(t',1)) % Biased Reference MSEtrn

MSEtrn00a = mean(var(t',0)) % Unbiased Reference: DOF "a"djusted

% Choose one depending on your version of NNTBX:

%net = newff(minmax(p),[ H O ]); % VERY OBSOLETE

%net = newfit(p, t, H); % OBSOLETE

net = fitnet(H); net = configure(net,p,t); % CURRENT

MSEgoal = 0.01*Ndof*MSEtrn00a/Ntrneq % R2agoal = 0.99

net.trainParam.goal= MSEgoal;

net.trainParam.min_grad = MSEtrn00/200;

net.divideFcn = '';

[ net tr Y E ] = train(net,p,t);

stopcrit = tr.stop

bestepoch = tr.best_epoch

MSEtrn = tr.perf(bestepoch)

MSEtrna = Ntrneq*MSEtrn/Ndof

NMSEtrn = MSEtrn/MSEtrn00

NMSEtrna = MSEtrna/MSEtrn00a

R2 = 1 -NMSE % ( R^2 = coefficient of determination ... See Wikipedia)

R2a = 1 - NMSEa % ( DOFA R^2)

If you are not satisfied with the R2a estimate of the fraction of target variance that is modeled, change H.

Hope this helps.

Thank you for formally accepting my answer

Greg

P.S. It is worthwhile to study the results of the following commands (no semocolon)

net = net

tr = tr