Robust delay-depent stability criteria for uncertain neural networks with two additive time-varying delay components

This paper considers the problem of robust stability of uncertain neural networks with two additive time varying delay components. The activation functions are monotone nondecreasing with known lower and upper bounds. By constructing of a modified augmented Lyapunov function, some new stability criteria are established in terms of linear matrix inequalities, which is easily solved by various convex optimization techniques. Compared with the existing works, the obtained criteria are less conservative due to reciprocal convex technique and an improved inequality, which provides more accurate upper bound than Jensen inequality for dealing with the cross-term. Finally, two numerical examples are given to illustrate the effectiveness of the proposed method.