Finite-time synchronization for Cohen-Grossberg neural networks with mixed time-delays

This paper aims to study the finite-time synchronization (i.e., synchronization in finite-time sense) of Cohen-Grossberg neural networks with mixed time delays (both time-varying discrete delay and infinite-time distributed delay). By constructing Lyapunov-Krasovskii functional candidates and using inequality techniques, some new sufficient conditions are derived to design the discontinuous state feedback controllers such that the addressed neural networks can be synchronized in a finite settling time, where the upper bounds of the settling time of synchronization are estimated. The effects of unknown or known time-delay are seriously taken into account, respectively, which lead to two different delay-independent discontinuous state feedback controllers. Thus our results can be applied to the finite-time synchronization of neural networks whether the time delay can be measured or not. As some special cases, our results also improve some recent works. Simulation results show the applicability and the advantages of the proposed finite-time controllers. (C) 2018 Elsevier B.V. All rights reserved.