State estimation of complex-valued neural networks with leakage delay: A dynamic event-triggered approach q

In this paper, the problem of state estimation is investigated for a class of discrete-time complex-valued neural networks (CVNNs) with both leakage delay and discrete time-varying delays. The signal transmis-sion from output sensors to state estimator is implemented via a shared wireless network with limited communication resources. For the aim of reducing the consumption of limited communication resources, the transmission strategy based on dynamic event-triggering is introduced to determine when the updat-ing of the output measurement should be carried out. By taking use of some properties of Hermitian matrix and constructing an appropriate Lyapunov-Krasovskii functional, a sufficient criterion is derived for ensuring the asymptotical stability of the estimation error system without separating the CVNN to its real-part system and imagination one is derived, which is quite different from those approach used in exiting literature. The gain matrix for estimator is designed by resorting to a set of feasible solutions of linear matrix inequalities (LMIs) with complex-valued variables. A numerical example and its simula-tion results are given to illustrate the validity of the theoretical result.(c) 2022 Elsevier B.V. All rights reserved.