LAUSR

This paper examines the robust stabilization problem of continuous-time delayed neural networks via the dissipativity-learning approach. A new learning algorithm is established to guarantee the asymptotic stability as well as the $(Q,S,R)$ - $\alpha$ -dissipativity of the considered neural networks. The developed result encompasses some existing results, such as $H_{\infty }$ and passivity performances, in a unified framework. With the introduction of a Lyapunov–Krasovskii functional together with the Legendre polynomial, a novel delay-dependent linear matrix inequality (LMI) condition and a learning algorithm for robust stabilization are presented. Demonstrative examples are given to show the usefulness of the established learning algorithm.