State estimation for neural networks with two additive time-varying delay components using delay-product-type augmented Lyapunov–Krasovskii functionals

Abstract

This paper focuses on the state estimation issue of neural networks with two additive time-varying delay components. By constructing a suitable augmented Lyapunov–Krasovskii functional, more time delay information is considered. Using the recently developed reciprocally convex inequality, some reciprocally convex combinations can be estimated more closely. The design conditions of state estimators are expressed as linear matrix inequalities (LMIs). The delay-product-type Lyapunov–Krasovskii functional is used to further reduce the conservativeness. With the help of state estimation method, the stability conditions of neural networks with two additive time-varying delay components are also developed. Finally, two numerical examples widely used in the literature show the effectiveness of the proposed method.