Fixed-time synchronization of discontinuous competitive neural networks with time-varying delays

In this article, the fixed-time (FXT) synchronization of discontinuous competitive neural networks (CNNs) involving time-varying delays is investigated. Firstly, two kinds of discontinuous FXT control schemes are proposed and two forms of Lyapunov function are constructed based on p-norm and 1-norm to discuss the FXT synchronization of CNNs. By means of nonsmooth analysis and some inequality techniques, some simple criteria are obtained to achieve FXT synchronization and the upper bound of the settling time with less conservativeness is provided. Furthermore, the effect of time scale on FXT synchronization of CNNs is considered. Lastly, some numerical results for an example are provided to demonstrate the derived theoretical results. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This article investigates the fixed-time (FXT) synchronization of discontinuous competitive neural networks (CNNs) with time-varying delays. Two types of discontinuous FXT control schemes are proposed, and two forms of Lyapunov function based on p-norm and 1-norm are constructed to analyze the FXT synchronization of CNNs. By employing nonsmooth analysis and inequality techniques, simple criteria for achieving FXT synchronization and an upper bound of the settling time with less conservativeness are derived. The effect of time scale on FXT synchronization of CNNs is also considered, and numerical results for an example are provided to validate the theoretical findings.