Stability analysis of delayed neural networks via a new integral inequality.

This paper focuses on stability analysis for neural networks systems with time-varying delays. A more general auxiliary function-based integral inequality is established and some improved delay-dependent stability conditions formulated in terms of linear matrix inequalities (LMIs) are derived by employing a suitable LyapunovKrasovskii functional (LKF) and the novel integral inequality. Three well-known application examples are provided to demonstrate the effectiveness and improvements of the proposed method.