Observer-based state estimation for memristive neural networks with time-varying delay

Abstract

This paper investigates the observer-based state estimation of memristive neural networks (MNNs) with time-varying delay. In order to obtain the accurate state information of the switching MNNs system, a new full-order state observer based on system measurement output is developed such that the reconstruction of states is achieved. By applying set-valued maps and differential inclusions, some sufficient conditions for delay-independent and delay-dependent asymptotic stability are obtained. The validity of theoretical results is verified via numerical examples.