论文标题
线性动力系统和机器学习的识别
Identification of linear dynamical systems and machine learning
论文作者
论文摘要
在卡尔曼（Kalman）的基本作品之后，动态系统识别的主题一直是现代控制的核心。实现理论一直是该领域的主要结果之一，有可能从输入输出关系中识别动态系统。机器学习概念的最新发展使人们恢复了识别的兴趣。在本文中，我们简要回顾了实现理论的结果，并开发了受机器学习概念启发的一些方法。
The topic of identification of dynamic systems, has been at the core of modern control , following the fundamental works of Kalman. Realization Theory has been one of the major outcomes in this domain, with the possibility of identifying a dynamic system from an input-output relationship. The recent development of machine learning concepts has rejuvanated interest for identification. In this paper, we review briefly the results of realization theory, and develop some methods inspired by Machine Learning concepts.
