How can i make a training of square pulse to neural network ? how

when i am training a square wave to a neural network ,network reply response below
</matlabcentral/answers/uploaded_files/24263/111.JPG> figure 1 : response of training of network ; figure 2: square pulse train ; why don't have same network's response and pulse train? code: fs = 100000 t = 0:1/fs:5; x2 = square(2*pi*t); net = newff([-1.5 1.5],[5,1],{'logsig','purelin'}); net= train(net,t,x2); Y=net(x2); subplot(211) plot(t,Y) axis([0 5 -1.2 1.2]);
subplot(212) plot(t,x2)

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Reformat so that the program will run when cut and pasted into the command line

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 Accepted Answer

% when i am training a square wave to a neural network , network reply response below % /matlabcentral/answers/uploaded_files/24263/111.JPG
% figure 1 : response of training of network ;
% figure 2: square pulse train ;
% why don't have same network's response and pulse train?
% code:
fs = 100000
t = 0:1/fs:5;
N = length(t) % 500,001
x2 = square(2*pi*t); % 0 <= 2*pi*t <= 31.416
net = newff([-1.5 1.5],[5,1],{'logsig','purelin'});
0. I don't have a toolbox with the square function. Therefore, I used x2 = sign(sin(2*pi*t))
1. You are trying to use a half of a million points to model a non-differentiable square wave over 5 periods.
2. For a sine wave
a. As few as ~12 points per period may be sufficient
b. At least 2 hidden node sigmoids are needed for each peak
3. You erroneously entered the input range to be [-1.5 1.5] instead of [ 0 5 ].
Hope this helps.
*Thank you for formally accepting my answer*
Greg

3 Comments

Hi Greg,thank you that help me
I implement whatever you said;
I put:
fs = 1000;
x2 = sign(sin(2*pi*t));
net = newff([0 5],[10,1],{'logsig','purelin'});
net= train(net,t,x2);
but the network's response is in contrast phase or Vice Versa of main function *so thanks Mr.Heath
Dr. Heath or Prof. Heath. However, I prefer Greg.
Label your plots Target and Output, respectively. Also increase the vertical range on the lower plot.
Greg
graph with increasing
the vertical range on the lower plot.

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