GA-Neural Network Hybridization

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Abul Fujail
Abul Fujail 2012-2-1
评论： Greg Heath 2017-1-30
How GA can be hybridized with Neural network (with reference to Matlab).
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Abul Fujail
Abul Fujail 2012-4-4
in='input_train.tra';
p=load(in);
p=transpose(p);
net=newff([.1 .9;.1 .9;.1 .9;.1 .9],[7,1], {'logsig','logsig'},'trainlm');
net=init(net);
tr='target_train.tra';
x=load(tr);
x=transpose(x);
net.trainParam.epochs=600;
net.trainParam.show=10;
net.trainParam.lr=0.3;
net.trainParam.mc=0.6;
net.trainParam.goal=0;
[net,tr]=train(net,p,x);
y=sim(net,p);
Some codes are shown above... i have 4 input vector and 1 target vector... i want to get the optimum weight with GA so that the mean square error between target and neural network predicted result is minimum. Please suggest me how the GA can be added with this neural network code..
thomas lass
thomas lass 2016-12-24
I need the full codes of GA can be hybridized with Neural network

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Greg Heath
Greg Heath 2012-2-3
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
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Shipra Kumar
Shipra Kumar 2017-1-30
编辑：Shipra Kumar 2017-1-30
greg how can u write y as a function. i am having similar difficulty while implementing ga-nn. would be glad if u could help
Greg Heath
Greg Heath 2017-1-30
y = B2+ LW*tansig( B1 + IW *x);

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