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...
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| Vydané v: | IEEE access Ročník 9; s. 139466 - 139472 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2169-3536, 2169-3536 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Ji, Yuejiang Lv, Lixin |
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| References | ref13 ref12 ref15 ref14 ref11 ref10 chen (ref3) 2021; 70 ref2 ref17 ref16 ref19 ref18 chen (ref4) 2021 ref24 ref23 billings (ref25) 2014 ref20 ref22 ref21 goodwin (ref26) 1984 ref8 ref7 chen (ref6) 2019; 64 ref9 söderström (ref5) 1989 ding (ref1) 2018 |
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| SubjectTerms | 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 |
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| Title | M-Decomposed Least Squares and Recursive Least Squares Identification Algorithms for Large-Scale Systems |
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