This paper gives a review of recent advances on memristive neural networks with emphasis
on the issues of stability analysis and state estimation. First, the concept of memristive neural …

This paper is concerned with the problem of extended dissipativity-based state estimation for
discrete-time Markov jump neural networks (NNs), where the variation of the piecewise time …

In this paper, the event-triggered state estimation problem is investigated for a class of
discrete-time stochastic memristive neural networks (DSMNNs) with time-varying delays and …

In this paper, the non-fragile dissipative state estimation is addressed for semi-Markov jump
inertial neural networks with reaction-diffusion. A semi-Markov jump model is used to …

This paper is concerned with the exponential stability and stabilization of memristive neural
networks (MNNs) with delays. First, we present some generalized double-integral …

This paper addresses the problem of H∞ state estimation for a class of stochastic memristor-
based neural networks with time-varying delays. Under the framework of Filippov solution …

The purpose of this work was to demonstrate the feasibility of building recurrent artificial
neural networks with hybrid complementary metal oxide semiconductor (CMOS)/memristor …

This paper is concerned about the finite-time stabilizability and instabilizability for a class of
delayed memristive neural networks (DMNNs). Through the design of a new nonlinear …

This paper investigates the problem of extended dissipative state estimation for memristor-
based neural networks (MNNs) with time-varying delay. Based on both nonsmooth analysis …

This paper investigates the exponential synchronization of delayed memristor-based neural
networks (MNNs) with discontinuous activation functions. Based on the framework of …