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A New Robust Stability Criterion for Dynamical Neural Networks with Mixed Time Delays

Main Authors: Guang Zhou, Shouming Zhong
Format: Article
Bahasa: eng
Terbitan: , 2013
Subjects:
Neural networks
Delayed systems
Lyapunov function
Stability analysis
Online Access: https://zenodo.org/record/1087886
ctrlnum 1087886
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language eng
format Journal:Article
Journal
author Guang Zhou
Shouming Zhong
title A New Robust Stability Criterion for Dynamical Neural Networks with Mixed Time Delays
publishDate 2013
topic Neural networks
Delayed systems
Lyapunov function
Stability analysis
url https://zenodo.org/record/1087886
contents In this paper, we investigate the problem of the existence, uniqueness and global asymptotic stability of the equilibrium point for a class of neural networks, the neutral system has mixed time delays and parameter uncertainties. Under the assumption that the activation functions are globally Lipschitz continuous, we drive a new criterion for the robust stability of a class of neural networks with time delays by utilizing the Lyapunov stability theorems and the Homomorphic mapping theorem. Numerical examples are given to illustrate the effectiveness and the advantage of the proposed main results.
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