By Topic

Synthesis for symmetric weight matrices of neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Saubhayana, M. ; Dept. of Electr. & Comput. Eng., Maryland Univ., College Park, MD, USA ; Newcomb, R.W.

A synthesis method to guarantee symmetric weight matrices for a class of neural networks (which includes the Hopfield neural network as a special case) is proposed. This fills in a gap in the Li-Michel-Porod's synthesis and guarantees asymptotic stability for a given set of linearly independent equilibrium points under Lyapunov's stability criteria.

Published in:

Circuits and Systems, 2003. ISCAS '03. Proceedings of the 2003 International Symposium on  (Volume:5 )

Date of Conference:

25-28 May 2003