Signal classifications using neural networks

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Ashikur on 21 Nov 2013
Commented: Greg Heath on 1 May 2016
Hello , can anyone help me with ideas about how to classify different time series signals using neural networks?
Are there any single input , single output classification network? For instance lets say I have some signals-
y1 = A1*sin(2*pi*f1*t);
y2 = A2*sin(2*pi*f2*t);
y3 = A3*sin(2*pi*f3*t)+A3*sin(2*pi*f4*t);
These signals are different in amplitude and frequencies. Can a neural network be designed that can classify these three type of signals in three different class?
Training -
Input Target
y1 ------- 1
y2 ------- 2
y3 ------- 3
Now I want to test some arbitrary signal and see if the network can classify it correctly. At least frequency wise.
Thanks in advance
  1 Comment
Greg Heath
Greg Heath on 25 Nov 2013
Are the amplitudes and frequencies known?
What is the dimensionality of the input vector?
An FFT approach might be more fruitful.

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Answers (1)

mae on 29 Apr 2016
i am having a similar issue .. i want to use neural networks for ECG signal classification and i am stuck

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