Fixed-time synchronization for inertial Cohen–Grossberg delayed neural networks: An event-triggered approach

Abstract

This paper addresses the fixed-time synchronization problem for inertial Cohen–Grossberg neural networks with external disturbances and time-varying delays. Compared with some existing works about fixed-time synchronization control methods, a novel controller is constructed with a dynamic exponential term, which can contain the two exponents. Moreover, taking into account the increase of network complexity as well as a huge quantity of data transmission, an event-triggered mechanism is introduced to effectively utilize the limited network bandwidth. By employing the variable transformation method, differential mean value theorem, and the fixed-time stability theory, some sufficient conditions ensuring the fixed-time synchronization of inertial Cohen–Grossberg neural networks are established. Finally, two numerical examples are given to illustrate the validity of the obtained results.