Research ArticleDelay-dependent global exponential robust stability for delayed cellular neural networks with time-varying delay

- This paper has presented improved delay-dependent exponential robust stability criteria for delayed cellular neural networks with time-varying delay.
- The traditional assumption that the derivatives of the delays are less than 1 is no longer required in our results.
- The proposed criteria are computationally attractive, and it provides less conservative results than the existing results.
- Numerical examples demonstrate that the proposed method is an improvement over the existing ones.

This paper investigates a class of delayed cellular neural networks (DCNN) with time-varying delay. Based on the Lyapunov-Krasovski functional and integral inequality approach (IIA), a uniformly asymptotic stability criterion in terms of only one simple linear matrix inequality (LMI) is addressed, which guarantees stability for such time-varying delay systems. This LMI can be easily solved by convex optimization techniques. Unlike previous methods, the upper bound of the delay derivative is taken into consideration, even if larger than or equal to 1. It is proven that results obtained are less conservative than existing ones. Four numerical examples illustrate efficacy of the proposed methods.