Dissipativity results for memristor-based recurrent neural networks with mixed delays

Abstract

This paper analyzes a class of memristor-based recurrent neural networks with mixed delays involving both discrete and distributed delays by constructing appropriate Lyapunov functionals and using some analytic techniques. Two new adequacy criteria concerning the dissipativity of the addressed neural networks are obtained. Finally, a numerical example is discussed in detail to substantiate our theoretical results.