Robust dissipativity analysis for uncertain neural networks with additive time-varying delays and general activation functions

This paper deals with the problem of delay-dependent robust dissipativity analysis for uncertain neural networks with additive time varying delays by using a more general activation function approach. Different from previous literature, some sufficient information on neuron activation function and additive time-varying delays have been considered. By constructing suitable Lyapunov-Krasovskii functionals (LKFs) with some new integral terms, and estimating their derivative by using newly developed single integral inequality that includes Jensen's inequality and Wirtinger-based integral inequality as a special case. A new delay-dependent less conservative global asymptotic stability and dissipative criteria have been established in the form of linear matrix inequalities (LMIs) technique. The effectiveness and advantages of the proposed results are verified by available standard numerical packages. (C) 2018 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.