报告题目：Bayesian Inference with Control Engineering Applications

报告人：加拿大工程院院士 黄彪教授

报告时间： 2018年5月24日（周四）上午9:30~11:00

报告地点：实验十五楼207室

报告摘要：

Bayesian theory, due to its mathematical rigor and application flexibility, has attracted great interests from both academia and practitioners. The original Bayesian rule, as a single formula, can evolve into pages of long mathematical derivations. Yet the end result provides very meaningful solutions to the practical problems. Although the control community may not be very familiar with the term “Bayesian”, it has been adopted by control scientists as early as the start of modern control. The most well known application of Bayesian theory in control engineering is Kalman filter which has been widely adopted by the control community. It is now commonly recognized that many control related problems can be formulated under Bayesian framework and readily solved. Bayesian inference is getting even more popular due to the growing interest in Big Data and Data Analytics. This presentation will give a historical overview of Bayesian methods in control engineering, current activities, and future trends. These will include Bayesian methods for modeling, estimation, fault detection & isolation, causality analysis, control performance monitoring, and soft sensors development.

报告人简介：

彪教授长期从事工业过程数据分析、 系统辨识、 控制系统性能评价、 故障检测与隔离以及软测量等方面的理论研究和工业实践， 取得了一系列原创性工作， 累积出版著作5部， 发表SCI 期刊论文300余篇。 他担任控制领域著名期刊Control Engineering Practice主编、 Journal of the Franklin Institute Subject主编、 Journal of Process Control副主编和Canadian Journal of Chemical Engineering副主编