Robust stability for delayed neural networks with nonlinear perturbation

Authors:
Li Xie;Tianming Liu;Jilin Liu;Weikang Gu;Stephen Wong
Affiliations:
Department of Information and Electronic Engineering, Zhejiang University, Hangzhou, P.R. China;Center for Bioinformatics, HCNR, Harvard Medical School, Boston, MA;Department of Information and Electronic Engineering, Zhejiang University, Hangzhou, P.R. China;Department of Information and Electronic Engineering, Zhejiang University, Hangzhou, P.R. China;Center for Bioinformatics, HCNR, Harvard Medical School, Boston, MA
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

Citing 5
Cited 1

Delay-dependent exponential stability analysis of delayed neural networks: an LMI approach

Neural Networks
Global exponential stability of delayed Hopfield neural networks

Neural Networks
Stability analysis of dynamical neural networks

IEEE Transactions on Neural Networks
Nonlinear measures: a new approach to exponential stability analysis for Hopfield-type neural networks

IEEE Transactions on Neural Networks
Some extensions of a new method to analyze complete stability of neural networks

IEEE Transactions on Neural Networks

Robust exponential stability analysis for unertain neural networks with time delay

IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application

Quantified Score

Hi-index	0.00

Visualization

Abstract

The problem of analysis of robust stability for time-delayed neural networks with nonlinear perturbation has been investigated via Lyapunov stability theory. The sufficient conditions for robust stability of neural networks with time delays have been developed. The exponential stability criterion for neural networks is also derived. The result includes the information on the state convergence degree of the neural networks. The robust stable criterion in this paper is presented in terms of linear matrix inequalities.