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This 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.

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