Reza:

Lets suppose that you want to train a NN for regression, some inputs and one target like this:

hiddenLayerSize = [6 2]; %two hidden layers with 6 and 2 hidden units

net = fitnet(hiddenLayerSize);

[net,tr] = train(net,inputs,targets);

The following code in MATLAB should print the NN configuration, weights, activation function and bias. Then you can build your equation...

clc;

fprintf('Network Configuration\n');

fprintf('Internal weights for each layer\n\n');

fprintf(':::: Input Layer/Hidden1 ::::\n');

for R=1:net.inputs{1}.size

fprintf('X%d\n',R)

for S=1:hiddenLayerSize(1)

fprintf('W(%d,%d)=%f\n',R,S,net.IW{1}(S,R));

end

fprintf('-------\n');

end

fprintf('Node Bias\n');

for R=1:net.layers{1}.dimensions

fprintf('b(%d)=%f\n',R,net.b{1}(R));

end

fprintf('-------\n');

fprintf('Activation function: %s\n',net.layers{1}.transferFcn);

fprintf('*********************************\n');

for R=2:net.numLayers-1

fprintf(':::: Input Hidden %d ::::\n',R);

%fprintf('%d,%d\n',R,R-1);

[NODES,IN_NODES]=size(net.LW{R,R-1});

for K=1:IN_NODES

fprintf('Node %d \n',K)

for S=1:NODES

fprintf('W(%d,%d)=%f\n',K,S,net.LW{R,R-1}(S,K));

end

fprintf('-------\n');

end

fprintf('Node Bias\n');

for m=1:net.layers{R}.dimensions

fprintf('b(%d)=%f\n',m,net.b{R}(m));

end

fprintf('-------\n');

fprintf('Activation function: %s\n',net.layers{R}.transferFcn);

fprintf('---------------------------------\n');

end

fprintf('*********************************\n');

fprintf(':::: Output Layer ::::\n');

R=R+1;

[NODES,IN_NODES]=size(net.LW{R,R-1});

for K=1:IN_NODES

fprintf('Node %d \n',K)

for S=1:NODES

fprintf('W(%d,%d)=%f\n',K,S,net.LW{R,R-1}(S,K));

end

fprintf('-------\n');

end

fprintf('Output bias\n');

fprintf('b=%f\n',net.b{net.numLayers}(1));

fprintf('-------\n');

fprintf('Activation function: %s\n',net.layers{R}.transferFcn);

fprintf('*********************************\n');

fprintf('Done...!\n');

Thanks for your good response.