Event-Triggered Synchronization of Multiple Fractional-Order Recurrent Neural Networks With Time-Varying Delays

This paper addresses the synchronization of multiple fractional-order recurrent neural networks (RNNs) with time-varying delays under event-triggered communications. Based on the assumption of the existence of strong connectivity or a spanning tree in the communication digraph, two sets of sufficient conditions are derived for achieving event-triggered synchronization. Moreover, an additional condition is derived to preclude Zeno behaviors. As a generalization of existing results, the criteria herein are also applicable to the event-triggered synchronization of multiple integer-order RNNs with or without delays. Two numerical examples are elaborated to illustrate the new results.

Automated summary

This paper investigates the synchronization of multiple fractional-order recurrent neural networks with time-varying delays under event-triggered communications, providing two sets of sufficient conditions and an additional condition to prevent Zeno behaviors. The criteria also extend to the event-triggered synchronization of multiple integer-order RNNs, with or without delays, and are illustrated through two numerical examples.