Abstract, New Squashing function...

[R. Srikanth]

>
>                             ABSTRACT
>
>       A BETTER ACTIVATION FUNCTION FOR ARTIFICIAL NEURAL NETWORKS
>
>     TR 93-8, Institute for Systems Research, University of Maryland
>
>   by David L. Elliott--  ISR, NeuroDyne, Inc., and Washington University
>                           January 29, 1993
> The activation function  s(x) = x/(1 + |x|) is proposed for use in
> digital simulation of neural networks, on the grounds that the
> computational operation count for this function is much smaller than
> for those using exponentials and that it satisfies the simple differential
> equation  s' = (1 + |s|)^2,  which generalizes the logistic equation.
> The full report, a work-in-progress, is available in LaTeX or PostScript
> form (two pages + titlepage) by request to delliott at src.umd.edu.
>
>

This squashing function while not widely in use, is and has been used by
few others. George Georgiou uses it for a complex back propagation network.
Not only does the activation function enable him to model a complex BP but
also seems to lend itself to easier implementation.

For more information on complex domain backprop, contact
Dr. George Georgiou at  georgiou at meridian.csci.csusb.edu


--


srikanth at cs.tulane.edu
Dept of Computer Science,
Tulane University,
New Orleans, La - 70118