MATLAB: How to drive out an equation from the trained neural network to use as an objective function for genetic algorithm

amir ghiami

Dear all,
I have some experimental data, by use of neural network I am trying to find a non-linear relation between my variants. I did that by applying below MATLAB codes.
p=dlmread('input.txt','',[0 0 3 11]);
t=dlmread('UTS.txt','',[0 0 0 11]);
net=newff(minmax(p),[5,7,1],{'purelin','logsig','purelin'},'traingd');
net.trainParam.show = 50;
net.trainParam.lr = 0.05;
net.trainParam.epochs = 10000;
net.trainParam.goal = 1e-4;
net.trainParam.mc = 0.5;
[net,tr]=train(net,p,t);
a=sim(net,p);
Now, I want to drive out the equation which correlates the inputs to the outputs so that I can use it as an objective function in order to be optimized by Genetic Algorithm. Once, I decided to use Wight matrixes and biases to obtain the function but I even could not find the right codes to obtain the weights and biases and also don't know the exact relation between the wights, biases, transfer functions, inputs and outputs.
I am getting confused.

Best Answer

You have a couple of options to acquire the underlying structure for further use, once you have trained your neural network:
  1. Use the view function to check the structure of your neural net. You can access the weight and bias values directly. Using the same notation that you used above, the input weights can be found via net.IW. Likewise, the layer weights and biases can be found via net.LW and net.b, respectively. If you understand the structure of your neural net, reconstruction should hopefully be fairly straightforward!
  2. Using the gensim command, you can create a Simulink block diagram of the neural network model. Since this is a most visual method of inspecting your trained network, the Simulink model might give you a more intuitive feel for how your model works.
Good luck!
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