Delay-dependent stability analysis for neural networks with additive time-varying delay components

This article studies the problem of stability analysis for neural networks (NNs) with two additive time-varying delay components. By taking both the independence and the variation of the two delay components into consideration, a more general Lyapunov functional is defined. By estimating the upper bound of the derivative of the Lyapunov functional more tightly, a less conservative delay-dependent stability criterion is established in terms of linear matrix inequalities. To reduce the computational complexity, a method for eliminating slack variables is provided, and then a simplified stability criterion is obtained. Some numerical examples are given to illustrate the effectiveness of the proposed method and the significant improvement over the existing results.