Data Filtering Based Maximum Likelihood Gradient Method for Multivariable Nonlinear Systems

Feiyan Chen; Kai Ji

doi:10.1109/CCDC49329.2020.9164205

Data Filtering Based Maximum Likelihood Gradient Method for Multivariable Nonlinear Systems

Feiyan Chen, Kai Ji

Department of Foundational Mathematics

Suzhou University of Science and Technology

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

Aiming at the problem of redundant parameters in the over-parameterized model, a maximum likelihood algorithm for a class of multi-input nonlinear systems is studied based on the data filtering technique. This paper develops a filtering based maximum likelihood generalized extended gradient algorithm for multivariable nonlinear systems with autoregressive moving average noises and a maximum likelihood generalized extended gradient algorithm for comparison. Finally, the simulation results indicate that the derived algorithms are more effective for identifying multivariable nonlinear systems.

Original language	English
Title of host publication	Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	999-1004
Number of pages	6
ISBN (Electronic)	9781728158549
DOIs	https://doi.org/10.1109/CCDC49329.2020.9164205
Publication status	Published - Aug 2020
Event	32nd Chinese Control and Decision Conference, CCDC 2020 - Hefei, China Duration: 22 Aug 2020 → 24 Aug 2020

Publication series

Name	Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020

Conference

Conference	32nd Chinese Control and Decision Conference, CCDC 2020
Country/Territory	China
City	Hefei
Period	22/08/20 → 24/08/20

Keywords

Filtering Technique
Gradient Method
Multivariable Nonlinear system
Parameter Estimation

Access to Document

10.1109/CCDC49329.2020.9164205

Cite this

Chen, F., & Ji, K. (2020). Data Filtering Based Maximum Likelihood Gradient Method for Multivariable Nonlinear Systems. In Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020 (pp. 999-1004). Article 9164205 (Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC49329.2020.9164205

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title = "Data Filtering Based Maximum Likelihood Gradient Method for Multivariable Nonlinear Systems",

abstract = "Aiming at the problem of redundant parameters in the over-parameterized model, a maximum likelihood algorithm for a class of multi-input nonlinear systems is studied based on the data filtering technique. This paper develops a filtering based maximum likelihood generalized extended gradient algorithm for multivariable nonlinear systems with autoregressive moving average noises and a maximum likelihood generalized extended gradient algorithm for comparison. Finally, the simulation results indicate that the derived algorithms are more effective for identifying multivariable nonlinear systems.",

keywords = "Filtering Technique, Gradient Method, Multivariable Nonlinear system, Parameter Estimation",

author = "Feiyan Chen and Kai Ji",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 32nd Chinese Control and Decision Conference, CCDC 2020 ; Conference date: 22-08-2020 Through 24-08-2020",

year = "2020",

month = aug,

doi = "10.1109/CCDC49329.2020.9164205",

language = "English",

series = "Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020",

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Chen, F & Ji, K 2020, Data Filtering Based Maximum Likelihood Gradient Method for Multivariable Nonlinear Systems. in Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020., 9164205, Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 999-1004, 32nd Chinese Control and Decision Conference, CCDC 2020, Hefei, China, 22/08/20. https://doi.org/10.1109/CCDC49329.2020.9164205

Data Filtering Based Maximum Likelihood Gradient Method for Multivariable Nonlinear Systems. / Chen, Feiyan; Ji, Kai.
Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 999-1004 9164205 (Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AU - Chen, Feiyan

AU - Ji, Kai

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N2 - Aiming at the problem of redundant parameters in the over-parameterized model, a maximum likelihood algorithm for a class of multi-input nonlinear systems is studied based on the data filtering technique. This paper develops a filtering based maximum likelihood generalized extended gradient algorithm for multivariable nonlinear systems with autoregressive moving average noises and a maximum likelihood generalized extended gradient algorithm for comparison. Finally, the simulation results indicate that the derived algorithms are more effective for identifying multivariable nonlinear systems.

AB - Aiming at the problem of redundant parameters in the over-parameterized model, a maximum likelihood algorithm for a class of multi-input nonlinear systems is studied based on the data filtering technique. This paper develops a filtering based maximum likelihood generalized extended gradient algorithm for multivariable nonlinear systems with autoregressive moving average noises and a maximum likelihood generalized extended gradient algorithm for comparison. Finally, the simulation results indicate that the derived algorithms are more effective for identifying multivariable nonlinear systems.

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KW - Gradient Method

KW - Multivariable Nonlinear system

KW - Parameter Estimation

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Chen F, Ji K. Data Filtering Based Maximum Likelihood Gradient Method for Multivariable Nonlinear Systems. In Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 999-1004. 9164205. (Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020). doi: 10.1109/CCDC49329.2020.9164205

Data Filtering Based Maximum Likelihood Gradient Method for Multivariable Nonlinear Systems

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