What is "THE STANDARD UNIVERSAL APPROXIMATOR NEURAL NETWORK "
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The "STANDARD" UNIVERSAL APPROXIMATOR NEURAL NETWORK is a hidden layer regression net with
1. A SINGLE hidden layer
2. A SINGLE bounded nonlinear hidden layer transfer function
3. A LINEAR output layer transfer function
I'm stating this because it is obvious that some believe that, for a universal approximator:
1. There has to be more than one hidden layer
and/or
2. The output layer transfer funtion has to be nonlinear.
Of course there are additional conditions on function finiteness, etc which I have omitted, but I think I have made my point.
Hope this Helps,
Greg
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