Stability Analysis of a General Class of Continuous-Time Recurrent Neural Networks

Authors:
Chaojin Fu;Zhongsheng Wang
Affiliations:
School of Mathematics and Statistics, Hubei Normal University, Huangshi, Hubei, China 435002;School of Automation, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, China 510665
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

Citing 4
Cited 0

Stability in asymmetric Hopfield nets with transmission delays

Physica D
Global attractivity in delayed Hopfield neural network models

SIAM Journal on Applied Mathematics
Global Stability of a General Class of Discrete-Time Recurrent Neural Networks

Neural Processing Letters
Multiperiodicity and Exponential Attractivity Evoked by Periodic External Inputs in Delayed Cellular Neural Networks

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents the results of stability analysis of a general class of continuous-time recurrent neural networks. The new stability results includes sufficient conditions for global asymptotic stability. With weaker conditions and less restrictive activation functions, the new stability results improve and extend existing ones. Discussions and examples are given to illustrate and compare the new results with the existing ones.