Paper on a compact, simple and efficient neural architecture

[Radu Dogaru]

Dear Connectionists,

The following paper is available and can be downloaded
from 
http://trixie.eecs.berkeley.edu/~radu_d/publications.html#p
or
http://trixie.eecs.berkeley.edu/~radu_d/dogaru_ijcnn99.pdf

All comments welcome.

			   
Perceptrons Revisited: The Addition of a Non-monotone Recursion
Greatly Enhances their Representation and Classification Properties

Radu Dogaru, Marinel Alangiu, Matthias Rychetsky and Manfred Glesner 


Abstract 
In this paper we describe a novel neural architecture and 
compare its representation and classification performances with 
classic solutions. It combines linear units  with a compact and
simple to implement non-linear transform defined as a finite recursion 
of simple non-monotonic functions. 
When such a nonlinear recursion replaces the standard output function 
of a perceptron-like structure, the representation capability of 
Boolean functions enhances beyond that of the standard linear 
threshold gates and arbitrary Boolean functions can be learned.
For example the realization of Parity function with 8 inputs 
requires only 8 synapses and 3 nonlinear units.   
While the use of nonlinear recursion at the output accounts for 
compact learning and memorization of arbitrary functions, it was 
found that good generalization capabilities are obtained when the 
nonlinear recursion is placed at the inputs.  It is thus concluded 
that the proper addition of a simple nonlinear structure to the 
well known linear perceptron removes most of its drawbacks, the 
resulting architecture being compact, easy to implement, 
and functionally equivalent to more sophisticated neural systems.


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Dr. Radu Dogaru c/o Prof. Leon O.  Chua 
University of California at  Berkeley
Department of Electrical Engineering and Computer Science
Cory Hall #1770
Berkeley, CA 94720 - 1770
Tel: (510) 643-8868
Fax: (510) 643-8869
E-mail: radu_d at fred.EECS.Berkeley.EDU
http://trixie.eecs.berkeley.edu/~radu_d
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