报告题目：Learning-based state estimation for complex dynamical networks

报告简介：This talk focuses on the state estimation problem for a class of complex dynamical networks (CDNs) with unknown nonlinear dynamics. A novel neural network based (NN-based) state estimator with non-fragility is constructed to fulfill the state estimation task. In order to save the communication resource, an event-triggered mechanism is also applied in the design of estimator. By employing the Lyapunov stability theory and inequality technique, several sufficient criteria are established for the existence of the desired exponentially ultimately bounded NN-based state estimator for CDNs. It is also proven that the proposed event-triggered mechanism can avoid Zeno phenomenon.

报告人：王子栋，现任英国伦敦Brunel University讲席教授，欧洲科学院院士，欧洲科学与艺术院院士，IEEE Fellow，International Journal of Systems Science主编，Neurocomputing主编。多年来从事控制理论、机器学习、生物信息学等方面研究，在SCI刊物上发表国际论文六百余篇。现任或曾任十二种国际刊物的主编、副编辑或编委。曾任旅英华人自动化及计算机协会主席、东华大学长江学者讲座教授、清华大学国家级专家。

报告时间：2022年8月19日（星期五）下午3:00

报告地点：腾讯会议，ID：673-680-488

主办单位：扬州大学数学科学学院

欢迎广大师生参加！