Novel Stability Results of Generalized Neural Networks Subject to Mixed Delays via Quadratic Polynomial Inequality

This dissertation considers the stability issue for generalized neural networks (GNNs) subject to mixed delays. Firstly, an appropriate Lyapunov-Krasovskii functionals (LKF) is constructed by containing two new delay-product-type (DPT) terms, augmented terms and double integral terms. Then, to cooperate with the established LKF effectively, an improved finite-interval quadratic polynomial inequality is utilized to handle the quadratic function negative-determination. As a consequence, a new less conservative stability condition is achieved. Finally, some well-studied simulation examples are included to show the superiority of the presented conditions.