Letters: An analysis on global robust exponential stability of neural networks with time-varying delays

Authors:
Jin-Liang Shao;Ting-Zhu Huang;Sheng Zhou
Affiliations:
School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China;School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China;School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, PR China
Venue:
Neurocomputing
Year:
2009

Citing 12
Cited 1

Matrix analysis

Matrix analysis
Topics in matrix analysis

Topics in matrix analysis
Global exponential stability of delayed Hopfield neural networks

Neural Networks
Global Robust Exponential Stability of Interval Neural Networks with Delays

Neural Processing Letters
Global robust stability of delayed neural networks with a class of general activation functions

Journal of Computer and System Sciences
Analysis of global exponential stability and periodic solutions of neural networks with time-varying delays

Neural Networks
Global robust stability of neural networks with time varying delays

Journal of Computational and Applied Mathematics
Letters: Comments and further improvements on "New LMI conditions for delay-dependent asymptotic stability of delayed Hopfield neural networks"

Neurocomputing
Letters: New global exponential stability results for neutral type neural networks with distributed time delays

Neurocomputing
Novel delay-dependent stability criteria of neural networks with time-varying delay

Neurocomputing
A global exponential robust stability criterion for interval delayed neural networks with variable delays

Neurocomputing
Robust stability for interval Hopfield neural networks with time delay

IEEE Transactions on Neural Networks

A New Sufficient Condition for Global Robust Stability of Delayed Neural Networks

Neural Processing Letters

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Abstract

The paper presents a new sufficient condition for the existence, uniqueness and global robust exponential stability of the equilibrium point for interval neural networks with time-varying delays. Theoretical analysis indicates that the obtained result improves and generalizes some previous results derived in the literatures. It is also shown by a numerical example that a recently reported result is invalid because the proof of it is not always right. Finally, a numerical example and the corresponding simulation are given to show the effectiveness of the obtained result.