Weight learning for ℋ∞$$ {\mathscr{H}}_{\infty } $$ stabilization of uncertain switched neural networks with external disturbance and reaction‐diffusion

Abstract

SummaryThis paper considers the stabilization of uncertain switched neural networks with external disturbance and reaction‐diffusion. A weight learning rule that ensures the 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.