MATLAB: GA optimization

annga

I have developed an neural network with 4 input nodes, 4 hidden nodes and one output node using feed forward back propagation network. Now I need to use GA to optimize and determine the maximum output. How to define the ANN model as the fitness function.

Best Answer

You can create a function handle to the network and then pass that function handle into GA. Note that GA will pass in a row vector, so if your NN accepts a column vector as input it will need to be transposed. Also, GA minimizes the objective function so we add the negative sign to flip it from a maximization problem to a minimization problem. For example:

objFcn = @(x) -sim(net,x');  % Function that simulates NN and returns output
[xOpt,fVal] = ga(objFcn, 4);  % Find the minimum of objFcn with 4 inputs

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MATLAB: GA-Neural Network Hybridization

I don't see how they can be combined to an advantage.

Just write the I/O relationship for the net in terms of input, weights and output: y = f(W,x). Then use the Global Optimization toolox to minimize the mean square error MSE = mean(e(:).^2) where e is the training error, e = (t-y) and t is the training goal.

Hope this helps.

Greg

MATLAB: How to calculate the error in Neural Network Back Propagation Network

Use as many defaults as possible

 MSEgoal = 0.01*mean(var(target',1))
 MinGrad = MSEgoal/100
 net = newff(input,target);
 net.trainParam.goal     = MSEgoal;
 net.trainParam.min_grad = MinGrad;
 [ net tr output error ] = train(net, input, target);

For details, search ANSWERS using the search word msegoal

Hope this helps.

Greg

Thank you for formally accepting my answer

Greg

Best Answer

Related Solutions

MATLAB: GA-Neural Network Hybridization

MATLAB: How to calculate the error in Neural Network Back Propagation Network

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