Abstract
This paper is concerned with studying H ∞ state estimation of memristor-based recurrent neural networks(MRNNs) with mixed delays. The MRNNs addressed is comprehensive to cover distributed-delays and time-varying delays in order to reflect the reality more closely and accurate. The delay-dependent design criteria is presented under which the resulting estimation error system is globally asymptotically stable and a prescribed performance is guaranteed in the H ∞ sense by the Jensen inequality, Wirtinger inequality and reciprocally convex approach. Finally, the simulation results confirm the effectiveness of Theorem 3.1 for the design of guaranteed performance H ∞ state estimator for MRNNs with mixed delays.
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