Detectability conditions for output-only subspace identification

IF 1.8 4区 数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Amirali Sadeqi, S. Moradi, Kourosh Heidari Shirazi
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引用次数: 7

Abstract

ABSTRACT The scope of output-only/blind identification is restricted to stochastic/statistical processes, but for the first time in this study, the detectability conditions for general output-only subspace identification are investigated. This aids the range of input sources to be extended in a much realistic manner, beyond the only stochastic inputs. For this purpose, the subspace framework is assigned to make a connection between the output signal contents and the LTI system order. A few substantial hypotheses and algebraic statements are propounded affirming the sufficiency of the genuine output sequences for the identification purpose. This can be perceived as the cornerstone of state-space model reconstruction. In order to consolidate the notions according to reality, several examples are studied and examined for different input classes with stochastic disturbance.
纯输出子空间识别的可检测性条件
摘要纯输出/盲识别的范围仅限于随机/统计过程,但在本研究中,首次研究了一般纯输出子空间识别的可检测性条件。这有助于以非常现实的方式扩展输入源的范围,而不仅仅是随机输入。为此,子空间框架被分配来在输出信号内容和LTI系统顺序之间建立连接。提出了一些实质性的假设和代数陈述,肯定了真实输出序列对于识别目的的充分性。这可以被视为状态空间模型重建的基石。为了根据实际情况巩固这些概念,对具有随机扰动的不同输入类的几个例子进行了研究和检验。
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来源期刊
CiteScore
3.80
自引率
5.30%
发文量
7
审稿时长
>12 weeks
期刊介绍: Mathematical and Computer Modelling of Dynamical Systems (MCMDS) publishes high quality international research that presents new ideas and approaches in the derivation, simplification, and validation of models and sub-models of relevance to complex (real-world) dynamical systems. The journal brings together engineers and scientists working in different areas of application and/or theory where researchers can learn about recent developments across engineering, environmental systems, and biotechnology amongst other fields. As MCMDS covers a wide range of application areas, papers aim to be accessible to readers who are not necessarily experts in the specific area of application. MCMDS welcomes original articles on a range of topics including: -methods of modelling and simulation- automation of modelling- qualitative and modular modelling- data-based and learning-based modelling- uncertainties and the effects of modelling errors on system performance- application of modelling to complex real-world systems.
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