Delay-dependent L2-L∞ state estimation for neural networks with state and measurement time-varying delays

This paper deals with the L-2-L-infinity state estimation for neural networks with time-varying delays. Considering the limited channel capacity or the long transmission time during signal transmission, a new system model with different state and measurement time-varying delays is established. Then, a new Lyapunov-Krasovskii functional (LKF) taking advantage of two types of delay information is constructed, Jensen integral inequality, Wirtinger-based integral inequality and convex combination approach are used to estimate the derivative of functional. Meantime, a novel L-2-L-infinity performance analysis method making full use of delay information is proposed, as a result, the delay-dependent conditions with less conservatism are obtained, under which the estimation error system is asymptotically stable with a prescribed L-2-L-infinity performance level. Numerical examples are given to show the effectiveness and the advantage of the proposed method. (C) 2018 Elsevier B.V. All rights reserved.