I tried the MS-Excel function (trendline), but it gave me a result far away from the original data.

*You*  are supposed to have the equation-the neural network allows you to check that the input data *are*, indeed,  associated to the output data, according to the model; but it's assumed that you have trained the neural network on a model you control.

Thanks Nicolis  for fast respond. In fact, I try to get an equation that can control the behavior of a certain data at any time. As example  to control the number of car accidents.

Jabar:

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');

Many Thanks Félix F. González-Navarro for your kind respond.

Nice code to build the NN with MatLab. But still How to get the equation of the result.

I used Neurosolution software to implement the NN and it is gave all the graphs and output. My focus how to get the equation of output( results).

Thanks again for your time and respond.

Regards

Thank you for great efforts. Waiting for more answers.