A generalized LMI-Based approach to the global exponential stability of recurrent neural networks with delay

Authors:
Yi Shen;Minghui Jiang;Xiaoxin Liao
Affiliations:
Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

Citing 3
Cited 0

Global stability for cellular neural networks with time delay

IEEE Transactions on Neural Networks
An analysis of global asymptotic stability of delayed cellular neural networks

IEEE Transactions on Neural Networks
A generalized LMI-based approach to the global asymptotic stability of delayed cellular neural networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new theoretical result on the global exponential stability of recurrent neural networks with delay is presented. It should be noted that the activation functions of recurrent neural network do not require to be bounded. The presented criterion, which has the attractive feature of possessing the structure of linear matrix inequality (LMI), is a generalization and improvement over some previous criteria. A example is given to illustrate our results.