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Two-stage multi-innovation stochastic gradient algorithm for multivariate output-error ARMA systems based on the auxiliary model.
- Source :
-
International Journal of Systems Science . Nov2019, Vol. 50 Issue 15, p2870-2884. 15p. - Publication Year :
- 2019
-
Abstract
- This paper investigates the parameter estimation problem for multivariate output-error systems perturbed by autoregressive moving average noises. Since the identification model has two different kinds of parameters, a vector and a matrix, the gradient algorithm cannot be used directly. Therefore, we decompose the original system model into two sub-models and proceed the identification problem by the collaboration between the two sub-models. By employing the gradient search and determining the optimal step-sizes, we present an auxiliary model based two-stage projection algorithm. However, in order to alleviate the sensitivity to the noise, we reselect the step-sizes and derive the auxiliary model based two-stage stochastic gradient (AM-2S-SG) algorithm. Based on the AM-2S-SG algorithm, an auxiliary model based two-stage multi-innovation stochastic gradient algorithm is proposed to generate more accurate estimates. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PARAMETER estimation
*ALGORITHMS
*COMPUTER simulation
Subjects
Details
- Language :
- English
- ISSN :
- 00207721
- Volume :
- 50
- Issue :
- 15
- Database :
- Academic Search Index
- Journal :
- International Journal of Systems Science
- Publication Type :
- Academic Journal
- Accession number :
- 140068872
- Full Text :
- https://doi.org/10.1080/00207721.2019.1690720