Least Squares Parameter Estimation for Dynamic Processes

Isermann, Rolf; Münchhof, Marco

doi:10.1007/978-3-540-78879-9_9

Identification of Dynamic Systems 2010

DOI: 10.1007/978-3-540-78879-9_9

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Least Squares Parameter Estimation for Dynamic Processes

Rolf Isermann

Marco Münchhof

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Cited by 2 publications

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Analysis of the properties of OLS-estimates in parameter identification of distributed dynamic processes

Matveev

Sirota

2020

J. Phys.: Conf. Ser.

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This work is devoted to analysis and research of OLS-estimates properties when identifying parameters of distributed dynamic processes. The study showed that at a low level of observation errors (1% and lower), the use of direct OLS estimates to identify parameters of distributed dynamic processes gives satisfactory results. At the same time, the displacement value is always slightly higher than the value of the standard deviation of the parameter estimate, which does not allow to neglect the displacement, especially at a high and average level of observation errors. The method of obtaining so-called alternative OLS-estimates is also proposed, which allows to reduce multicollinearity in sample statistics and at any level of observation errors significantly reduce standard error of parameter estimation.

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Analysis of the properties of OLS-estimates in parameter identification of distributed dynamic processes

Matveev

Sirota

2020

J. Phys.: Conf. Ser.

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Data-Driven Distributed Information-Weighted Consensus Filtering in Discrete-Time Sensor Networks With Switching Topologies

Wei

Fan

et al. 2023

IEEE Trans. Cybern.

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Least Squares Parameter Estimation for Dynamic Processes

Cited by 2 publications

References 37 publications

Analysis of the properties of OLS-estimates in parameter identification of distributed dynamic processes

Analysis of the properties of OLS-estimates in parameter identification of distributed dynamic processes

Data-Driven Distributed Information-Weighted Consensus Filtering in Discrete-Time Sensor Networks With Switching Topologies

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