Title :
Global Robust Stability Criteria for Interval Delayed Neural Networks Via an LMI Approach
Author :
Li, Chuandong ; Liao, Xiaofeng
Author_Institution :
Dept. of Comput. Sci. & Eng., Chongqing Univ.
Abstract :
The problem of the global robust stability of delayed interval neural networks is considered. We first illustrate that the results given by Arik recently are unjustified, and then a revised version is proposed in light of Arik´s idea. By taking an approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI), several criteria for determining the robust exponential stability of delayed neural networks are derived, which provide an easily verified guideline. Moreover, the exponential convergence rate is estimated via LMI-Toobox in Matlab. The theoretical analysis and numerical simulations show that the new results are less conservative and less restrictive than the ones reported recently in the literature
Keywords :
asymptotic stability; delays; linear matrix inequalities; neural nets; Lyapunov-Krasovskii functional; global robust stability criteria; interval delayed neural networks; linear matrix inequality; robust exponential stability; Convergence; Guidelines; Hydrogen; Linear matrix inequalities; Neural networks; Numerical simulation; Robust stability; Robustness; Stability criteria; Symmetric matrices; Interval delayed neural networks (IDNNs); Lyapunov–Krasovskii functional; linear matrix inequality (LMI); robust exponential stability;
Journal_Title :
Circuits and Systems II: Express Briefs, IEEE Transactions on
DOI :
10.1109/TCSII.2006.880335