By Topic

On solving constrained optimization problems with 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

3 Author(s)
Glazos, M.P. ; Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA ; Hui, S. ; Zak, S.H.

We analyze a class of neural networks that solve convex programming problems. In carrying out the analysis we use concepts from the theory of differential equations with discontinuous right-hand sides and Lyapunov stability theory. We show that irrespective of the initial state of the network the state converges to a solution of the convex programming problem. The dynamic behavior of the networks is illustrated by two numerical examples

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:7 )

Date of Conference:

27 Jun-2 Jul 1994