M-Decomposed Least Squares and Recursive Least Squares Identification Algorithms for Large-Scale Systems

Two M-decomposed based identification algorithms are proposed for large-scale systems in this study. Since the least squares algorithms involve matrix inversion calculation, they can be inefficient for large-scale systems whose information matrices are ill-conditioned. To overcome this difficulty, t...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE access Ročník 9; s. 139466 - 139472
Hlavní autori: Ji, Yuejiang, Lv, Lixin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
computational effort
Convergence
Decomposition
large-scale system
Large-scale systems
Least squares
M-decomposed least squares
Mathematical analysis
Mathematical model
Matrix decomposition
Parameter estimation
Parameters
sub-parameter-vector
Symmetric matrices
ISSN:2169-3536, 2169-3536
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Two M-decomposed based identification algorithms are proposed for large-scale systems in this study. Since the least squares algorithms involve matrix inversion calculation, they can be inefficient for large-scale systems whose information matrices are ill-conditioned. To overcome this difficulty, the M-decomposed based least squares algorithm is developed, where the parameter vector is divided into M sub-vectors. Each sub-vector is estimated using the least squares algorithm, with the assumption that the other sub-vectors are known. The proposed algorithm has less computational efforts than those of the traditional least squares algorithm. To update the parameters with new arrived data, an M-decomposed based recursive least squares algorithm is also provided, this algorithm avoids matrix inversion calculation thus is more efficient. The simulation examples show the effectiveness of the proposed algorithms.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3113707