Auxiliary matrix weighted functions approach and its application to delayed neural networks system

This article analyzes the exponential stability analysis of delayed neural networks. By combining novel inequality handling techniques and effectively utilizing delay and system state information, novel auxiliary matrix weighted functions is proposed. Additionally, an enhanced Lyapunov-Krasovskii functional is established, which containing exponential terms. This approach yields a novel criterion for determining the exponential stability of neural network systems, exhibiting reduced conservatism compared to existing methodologies. Finally, a series of numerical examples strongly validate that the innovative method is effective.