Decomposition‐based multiinnovation gradient identification algorithms for a special bilinear system based on its input‐output representation

Summary This article considers the parameter estimation for a special bilinear system with colored noise. Its input‐output representation is derived by eliminating the state variables in the bilinear system. Based on the input‐output representation of the bilinear system, a multiinnovation generaliz...

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Veröffentlicht in:International journal of robust and nonlinear control Jg. 30; H. 9; S. 3607 - 3623
Hauptverfasser: Wang, Longjin, Ji, Yan, Yang, Hualin, Xu, Ling
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bognor Regis Wiley Subscription Services, Inc 01.06.2020
Schlagworte:
Algorithms
bilinear system
Computer simulation
Decomposition
hierarchical identification
Identification
multiinnovation
Parameter estimation
Parameter identification
Representations
State variable
stochastic gradient
ISSN:1049-8923, 1099-1239
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Zusammenfassung:Summary This article considers the parameter estimation for a special bilinear system with colored noise. Its input‐output representation is derived by eliminating the state variables in the bilinear system. Based on the input‐output representation of the bilinear system, a multiinnovation generalized extended stochastic gradient (MI‐GESG) algorithm is proposed by using the multiinnovation identification theory. Furthermore, a decomposition‐based multiinnovation (ie, hierarchical multiinnovation) generalized extended stochastic gradient identification (H‐MI‐GESG) algorithm is derived to enhance the parameter estimation accuracy by using the hierarchical identification principle, and a GESG algorithm is presented for comparison. Compared with the existing identification algorithms for the bilinear system, the proposed MI‐GESG and H‐MI‐GESG algorithms can generate more accurate parameter estimation. Finally, a simulation example is provided to verify the effectiveness of the proposed algorithms.
Bibliographie:Funding information
National Natural Science Foundation of China, 61803049
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.4959