Stability analysis of neural networks with time-varying delay using a new augmented Lyapunov–Krasovskii functional

Abstract

This paper examines the problem of asymptotic stability of continuous neural networks with time-varying delay via a new Lyapunov–Krasovskii functional (LKF). First, a suitable quadratic functional is constructed, which coordinates with the use of the orthogonal-polynomials-based integral inequality. Second, the novel proposed LKF contains more state vectors of neural networks, so that more state information can be exploited adequately. By combining the new proposed LKF and orthogonal-polynomials-based integral inequality, novel delay-dependent stability criteria with less conservatism are established in the form of linear matrix inequalities (LMIs). Finally, two commonly-used numerical examples are provided to show the effectiveness and improvement of the proposed criteria.