function [criterion,sse3,g,A3,W3,b3,W4,b4,W5] = ffnet9fun_elman(beta2);
global P T nlayer nneuron1 nneuron2 nneuron3;
[rt ct] = size(T); rt = min([rt ct]);
[rp cp] = size(P);
cp1 = cp+1;
nn1 =  nneuron1 * nneuron1;
beta = beta2(1:end- nn1);
beta1 = beta(1:nneuron1*cp);
W3 = reshape(beta1,cp,nneuron1);
b3 = beta(nneuron1*cp+1:nneuron1*cp+nneuron1);
W4 = beta(nneuron1*cp+nneuron1+1:end-ct);
b4 = beta(end-ct+1:end);
W4 = reshape(W4,nneuron1,ct);
b4 = beta(end-ct+1:end);
W5 = beta2(end-nn1+1:end);
W5 = reshape(W5,nneuron1,nneuron1);
A31 =  P* W3 +  kron(ones(rt,1),b3);
A32 = A31(2:end,:) + A31(1:end-1,:) * W5;
A33  =   1 ./ (1 + exp(-A32));
A34  =  A33  * W4  +   kron(ones(length(A33),1),b4);
A3 = [T(1,:); A33];
T = T;
A3 = A3; A3 = real(A3);
TT = T;
AA3 = A3;
err3 = TT- AA3;
sse3 =  sum(err3 .^2);
sigmaerr = inv(cov(err3));
criterion = sse3 * sigmaerr * sse3';
criterion = real(criterion);
criterion = real(criterion);
ssrsq3 = var(A3) ./ var(T);
ssrsq3 = ssrsq3;
g = [];