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

作者：Liu, Peng; Wang, Jun^*; Zeng, Zhigang

来源：IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(8): 4620-4630.

DOI：10.1109/TNNLS.2021.3116382

• Synchronization
• Protocols
• Delays
• Recurrent neural networks
• Delay effects
• Control systems
• Optimization
• Event-triggered communication
• fractional-order systems
• recurrent neural networks (RNNs)
• synchronization

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.

• 单位
  华中科技大学