Neural Comp. Sign up for ETOCS
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Dreyfus, G.
Right arrow Articles by Idan, Y.
Right arrow Search for Related Content
PubMed
Right arrow Articles by Dreyfus, G.
Right arrow Articles by Idan, Y.

Neural Computation, Vol 10, 133-165, Copyright © 1998 by The MIT Press

LETTERS

A Canonical Form of Nonlinear Discrete-Time Models

Gérard Dreyfus and Yizhak Idan

Discrete-time models of complex nonlinear processes, whether physical, biological, or economical, are usually under the form of systems of coupled difference equations. In analyzing such systems, one of the first tasks is to find a state-space description of the process -- that is, a set of state variables and the associated state equations. We present a methodology for finding a set of state variables and a canonical representation of a class of systems described by a set of recurrent discrete-time, time-invariant equations. In the field of neural networks, this is of special importance since the application of standard training algorithms requires the network to be in a canonical form. Several illustrative examples are presented.




HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS
Copyright © 1998 by The MIT Press.