Global Robust Stability Criteria for Interval Delayed Neural Networks Via an LMI Approach

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