Neural Computation, Vol 8, 1767-1786, Copyright © 1996 by The MIT Press
Autonomous design of artificial neural networks by Neurex
F Michaud and R Gonzales Rubio
Department of Electrical and Computer Engineering, Universite de Sherbrooke, Quebec, Canada.
Artificial neural networks (ANN) have been demonstrated to be increasingly
more useful for complex problems difficult to solve with conventional
methods. With their learning abilities, they avoid having to develop a
mathematical model or acquiring the appropriate knowledge to solve a task.
The difficulty now lies in the ANN design process. A lot of choices must be
made to design an ANN, and there are no available design rules to make
these choices directly for a particular problem. Therefore, the design for
an ANN demands a certain number of iterations, mainly guided by the
expertise and the intuition of the developer. To automate the ANN design
process, we have developed Neurex, composed of an expert system and an ANN
simulator. Neurex autonomously guides the iterative ANN design process. Its
structure tries to reproduce the design steps done by a human expert in
conceiving an ANN. As a whole, the Neurex structure serves as a framework
to implement this expertise for different learning paradigms. This article
presents the system's general characteristics and its use in designing ANN
using the standard backpropagation learning law.
