Extended dissipativity state estimation for generalized neural networks with time-varying delay via delay-product-type functionals and integral inequality

In this paper, the problem of extended dissipativity state estimation for delayed generalized neural networks (GNNs) is investigated. Firstly, in order to facilitate the use of more information of time-varying delay, a class of wdelay-product-type Lyapunov-Krasovskii functional (LKF) is proposed. Secondly, in order to accurately estimate the upper bound of the time-derivative of the constructed LKF, a delay-product-type integral inequality is proposed, then some sufficient conditions are obtained to guarantee the extended dissipativity state estimation for delayed GNNs. Moreover, the extended dissipativity state estimation can be used to tackle the problem of H-infinity performance state estimation, passivity performance state estimation, L-2-L-infinity performance state estimation, and (Q, S, R)-gamma-dissipativity state estimation for delayed GNNs. Finally, simulations are provided to illustrate the effectiveness of the proposed method. (C) 2021 Elsevier B.V. All rights reserved.