Delay-dependent global exponential stability for neural networks with time-varying delay

Abstract

This paper deals with the global exponential stability problem of neural networks with time-varying delay. A novel Lyapounov–Krasovskii functional (LKF), which contains a common double integral term, an augmented double integral term and two delay-product-type terms, is constructed to analyze the exponential stability. An auxiliary function-based integral inequality (AFBII) and two special forms of it are applied to estimate the upper bounds of single integral terms produced in the time derivation of the LKF candidate. By using the novel LKF and AFBII, some new cross terms of matrix variables are included in linear matrix inequalities (LMIs). As a result, a less conservative delay-dependent global exponential stability criterion is proposed. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed criterion.