Finite-time and fixed-time synchronization analysis of fuzzy Cohen-Grossberg neural networks with discontinuous activations and parameter uncertainties

摘要

This paper mainly investigates the finite-time and fixed-time synchronization for a class of delayed fuzzy Cohen-Grossberg neural networks with discontinuous activations and parameter uncertainties. First of all, the equivalent transformation is made for the proposed neural network system in order to deal with the amplification functions. By using the Filippov discontinuity theories and non-smooth Lyapunov-Krasovskii functional, some sufficient algebraic criteria are given for the selection of the designed switching adaptive control parameters to accomplish the finite-time synchronization, and the upper bound of the settling time can be estimated. After that, based on the inequality technique and by designing a discontinuous state-feedback control law, some sufficient conditions are derived to achieve synchronization within a fixed time, and the settling time is given, which is independent on initial values. The Cohen-Grossberg neural networks combining fuzzy model with discontinuous activations and parameter uncertainties are considered for the first time in the literature. Finally, a numerical example is presented to illustrate the effectiveness of the proposed synchronization strategies. (c) 2020 European Control Association. Published by Elsevier Ltd. All rights reserved.