报告人：涂正文

时 间：2026年5月17日 15:00

地 点：理科楼 L2620

报告摘要：In this talk, an adaptive dynamic event-triggered mechanism will be designed to achieve H∞ state estimation for a class of uncertain discrete-time quaternion-valued neural networks. The proposed mechanism adaptively updates the triggering threshold in response to the system output to improve communication efficiency. Sevral criteria are established to guarantee the boundedness of the estimation error within a finite time interval under the prescribed H∞ performance. Finally, simulation results will be presented to verify the effectiveness of the obtained results.