Weight learning for H-8 stabilization of uncertain switched neural networks with external disturbance and reaction-diffusion

This paper considers the H-infinity stabilization of uncertain switched neural networks with external disturbance and reaction-diffusion. A weight learning rule that ensures the H-infinity stability of the network is proposed utilizing the Lyapunov-Krasovskii functional method. Then, a useful lemma concerning the equivalence of matrix inequities is derived. With the aid of the lemma and Schur complement, a more concise existence condition on the learning rule is developed. Finally, theoretical comparisons and a numerical example are given, which show that the obtained results are extensions and improvements of an existing result.