Event-Triggered Synchronization of Multiple Fractional-Order Recurrent Neural Networks With Time-Varying Delays
Peng Liu, Jun Wang, Zhigang Zeng
Abstract
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.
Topics & Concepts
Synchronization (alternating current)GeneralizationDigraphRecurrent neural networkComputer scienceEvent (particle physics)Artificial neural networkZeno's paradoxesControl theory (sociology)MathematicsArtificial intelligenceComputer networkControl (management)Discrete mathematicsPhysicsGeometryQuantum mechanicsMathematical analysisChannel (broadcasting)Neural Networks Stability and SynchronizationAdvanced Memory and Neural Computingstochastic dynamics and bifurcation