Scheduled System Maintenance:
On April 27th, single article purchases and IEEE account management will be unavailable from 2:00 PM - 4:00 PM ET (18:00 - 20:00 UTC).
We apologize for the inconvenience.
By Topic

Global exponential stability of recurrent neural networks for solving optimization and related problems

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.

The purchase and pricing options are temporarily unavailable. Please try again later.
2 Author(s)
Youshen Xia ; Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, Shatin, China ; Jun Wang

Global exponential stability is a desirable property for dynamic systems. The paper studies the global exponential stability of several existing recurrent neural networks for solving linear programming problems, convex programming problems with interval constraints, convex programming problems with nonlinear constraints, and monotone variational inequalities. In contrast to the existing results on global exponential stability, the present results do not require additional conditions on the weight matrices of recurrent neural networks and improve some existing conditions for global exponential stability. Therefore, the stability results in the paper further demonstrate the superior convergence properties of the existing neural networks for optimization

Published in:

Neural Networks, IEEE Transactions on  (Volume:11 ,  Issue: 4 )