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How we can define the number of expansion of first input by using trigonometric functional link artificial neural network?

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In trigonometric functional link artificial neural network, each input sample is expanded to N sine terms, N cosine terms plus the sample itself. How we can define N ?

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Greg Heath
Greg Heath el 16 de En. de 2016
From Fourier Series
N = T/dt
T = length of sample
dt = sampling time
Hope this helps.
Thank you for formally accepting my answer
Greg
  4 comentarios
coqui
coqui el 24 de En. de 2016
I need to predict one day ahead by using functional link artificial neural network with hyperbolic tangent transfer function in output layer. what about the use of three expansion of first input Y1, i.e. are y1=Y1, y2=cos(πY1), y3=sin(πY1)???
Greg Heath
Greg Heath el 25 de En. de 2016
1. I AM NOT FAMLIAR WITH THE FUNCTIONAL LINK NET AND DON'T SEE THE ADVANTAGE OF ADDING THE FOURIER TERMS.
2. WHAT YOU HAVE WRITTEN ABOVE FOR Y2 AND Y3 MAKES ASOLUTELY NO SENSE TO ME. THEY ARE NOT TERMS IN THE EXPANSION OF Y1.

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