How can I fix minmum mse requirement for neural network training mannually rather using default requirement?

I wish to stop traing automatically when mse falls below 0.0001, but system is waiting till it becomes 10^-14 or so.

Answer 1

[Greg Heath]

 [ I N ] = size(x)
 [ O N ] = size(t)

% Reference MSEs (From Naïve Constant Output Model y00 = mean(ttrn,2))

 Ntrn      = N-2*(0.15*N)       % Default no. of training examples
 MSEtrn00  = mean(var(ttrn',1)) % Biased training target variance (divide by Ntrn)
 MSEtrn00a = mean(var(ttrn',0)  % Degree of freedom "A"djusted (divide by Ntrn-1)

% Number of estimation degrees of freedom (See Wikipedia)

 Ntrneq    = Ntrn*O             % prod(size(ttrn)) is number of training equations
 Nw        = (I+1)*H+(H+1)*O    % Number of unknown weights for an I-H-O node topology
 Ndof      = Ntrneq-Nw          % Not useful when Nw >= Ntrneq

 etrn    = ttrn-ytrn;           % Training error
 MSEtrn  = sse(etrn)/Ntrneq     % Biased MSE
         = mse(etrn)

 MSEtrna = sse(etrn)/Ndof       % DOFA  MSE
         = Ntrneq*MSEtrn/Ndof

% Normalized MSEs

 NMSEtrn  = MSEtrn/MSEtrn00
 NMSEtrna = MSEtrna/MSEtrn00a

% Practical training goal

 NMSEtrna <= 0.01  % Independent of target scaling

 net.trainParam.goal = 0.01*Ndof*MSEtrn00a/Ntrneq

Hope this helps.

Thank you for formally accepting my answer

Greg

[Balaji]

thank you, your answer is very useful