Function Approximation with AI methodologies
5/2/13
I found out how to get the NN to display the funtion it has created. you use the sim command to simulate the results example:
x=0:0.05:2;
y=humps(x);
P=x; T=y;
plot(P, T, 'x')
xlabel('time'); ylabel('output'); title('Original Function');
net = fitnet(3, 'trainlm');  %fitnet is feedforward network 'n' neurons, and training function
%default settings; 1000 iterations
net = train(net, P, T);
view(net); %diagram of network
y = net(x);
a= sim(net, P);
% Plot result and compare
plot(P, a-T, P, a, P, T); grid;
legend('error', 'NN function', 'Original');
If you inrease the number of neurons you can see the NN fuction becoming closer to the orignal, also the error line is closer to zero.
humps(x) is a demo function which is equivalent to :
y = 1 ./ ((x-.3).^2 + .01) + 1 ./ ((x-.9).^2 + .04) - 6;
Alterantively you can use the data set
[x, y] = simplefit_dataset;(no need for the x=0:0.05:2 with this)

Source: Function Approximation with AI methodologies

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