TITLE:   Training Asymptotically Stable 
	 Recurrent Neural Networks
	 
AUTHORS: Nikitas J. Dimopoulos, John Dorocicz, 
	 Chris Jubien, and Stephen W. Neville
	 
IN:      Intelligent Automation and Soft 
	 Computing, Vol. 2, No. 4, 1996, 
	 pp. 375-388. 
	 

ABSTRACT 

In this work we present a class of recurrent networks
which are asymptotically stable. For these networks, 
we discuss their similarity with certain structures 
in the central nervous system, and prove that if an 
interconnection pattern that does not allow excitatory 
feedback is used, then the resulting recurrent neural 
network is stable. We introduce a training methodology
for networks belonging to this class, and use it to 
train networks that successfully identify a number 
nonlinear systems.