Improved exponential stability of time delay neural networks via separated-matrix-based integral inequalities.

This paper studies the exponential stability of neural networks with time delays. A separated-matrix-based integral inequality is proposed to incorporate more delay information. It not only reflects the information of each component in the state-related vector but also considers the cross terms among the three components, significantly reducing the inherent conservativeness of traditional methods. By constructing a Lyapunov-Krasovskii functional with separation-matrix-based integral and a linear matrix inequality framework via quadratic negative definiteness, less conservative stability criteria are established. Two numerical examples demonstrate the method superiority in maximum allowable delay bounds and computational efficiency compared to existing approaches.