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从碎片化量子化测量中进行节的系统识别

Omar M Sleem¹, Constantino M Lagoa¹

¹Department of Electrical Engineering, Pennsylvania State University, State College, PA 16801, USA.

International journal of control

|September 23, 2024

概括

本研究引入了一种新的方法,用于识别使用量化数据的线性时间不变系统. 该方法有效地处理杂和分散的观测,实现节的系统识别.

科学领域:

信号处理信号处理
控制系统工程控制系统工程
系统识别系统识别系统

背景情况:

量子化是一种非线性,不可逆转的过程,使传统系统识别复杂化.
现有的方法与杂,碎片化和定量化的观测数据作斗争.

研究的目的:

从量化观测中开发一种用于从量化观测中精简的线性时间不变 (LTI) 系统识别的方法.
为应对噪音数据和潜在的数据碎片化所带来的挑战.
根据可用的信息和先前知识,确定最低级系统.

主要方法:

使用一个先验的信息关于系统极点在一个紧的集合.
采用一个交替方向方法的乘法器 (ADMM) 算法.
解决了一个混合的1,2准规范客观问题.

主要成果:

提出的基于ADMM的方法成功地从量化,噪音和碎片化数据中识别了LTI系统.
实现节的系统识别,偏好低级模型.
在解决方案稀疏性方面,与 $l$ ₁最小化相比,表现出更高的性能.

结论:

开发的ADMM方法为在量子化约束下系统识别提供了有效的解决方案.
该方法提供了一种可靠的方式来处理在系统建模中不完美的观测数据.
这项工作推动了系统识别领域的发展,通过使用有限的量化信息实现了准确的建模.

关键词:

在这个问题上,ADMMMM是ADMM.量子化是指量化过程中的一个过程.稀缺性是一种稀缺性.系统识别系统识别

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