LMI-Based Stability Criteria With Auxiliary Matrices for Delayed Recurrent Neural Networks

In this note, the global asymptotic stability for delayed recurrent neural networks is addressed with a new Lyapunov-Krasovskii function. New delay-independent linear matrix inequality (LMI)-based conditions for global asymptotic stability are derived. A key feature of the new approach is the introduction auxiliary matrices, which can provide useful and less conservative results. This feature also enables us to cast a series of previous LMI-based results into even more general framework, logic flow of ideas and comparisons are thus easily shown. Three numerical examples show the effectiveness of the proposed method.