Convergence of the recursive identification algorithms for multivariate pseudo‐linear regressive systems: Convergence of the recursive identification algorithms for multivariate pseudo-linear regressive systems
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| Title: | Convergence of the recursive identification algorithms for multivariate pseudo‐linear regressive systems: Convergence of the recursive identification algorithms for multivariate pseudo-linear regressive systems |
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| Authors: | Xuehai Wang, Feng Ding |
| Source: | International Journal of Adaptive Control and Signal Processing. 30:824-842 |
| Publisher Information: | Wiley, 2015. |
| Publication Year: | 2015 |
| Subject Terms: | Systems theory, control, pseudo-linear regressive model, stochastic gradient algorithm, martingale convergence theorem, parameter estimation, 16. Peace & justice, least squares algorithm |
| Description: | SummaryThe performance analysis of the recursive algorithms for the multivariate systems with an autoregressive moving average noise process is still open. This paper analyzes the convergence of two recursive identification algorithms, the multivariate recursive generalized extended least squares algorithm and the multivariate generalized extended stochastic gradient algorithm, for pseudo‐linear multivariate systems and proves that the parameter estimation errors consistently converge to zero under persistent excitation conditions. The simulation results show that the proposed algorithms work well. Copyright © 2015 John Wiley & Sons, Ltd. |
| Document Type: | Article |
| File Description: | application/xml |
| Language: | English |
| ISSN: | 1099-1115 0890-6327 |
| DOI: | 10.1002/acs.2642 |
| Access URL: | https://zbmath.org/8036807 https://doi.org/10.1002/acs.2642 https://dlnext.acm.org/doi/10.1002/acs.2642 https://onlinelibrary.wiley.com/doi/10.1002/acs.2642 |
| Rights: | Wiley Online Library User Agreement |
| Accession Number: | edsair.doi.dedup.....c7de1fac9f99f6f3b58ff82417f02de0 |
| Database: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | SummaryThe performance analysis of the recursive algorithms for the multivariate systems with an autoregressive moving average noise process is still open. This paper analyzes the convergence of two recursive identification algorithms, the multivariate recursive generalized extended least squares algorithm and the multivariate generalized extended stochastic gradient algorithm, for pseudo‐linear multivariate systems and proves that the parameter estimation errors consistently converge to zero under persistent excitation conditions. The simulation results show that the proposed algorithms work well. Copyright © 2015 John Wiley & Sons, Ltd. |
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| ISSN: | 10991115 08906327 |
| DOI: | 10.1002/acs.2642 |