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New gradient based identification methods for multivariate pseudo-linear systems using the multi-innovation and the data filtering.
- Source :
-
Journal of the Franklin Institute . Feb2017, Vol. 354 Issue 3, p1568-1583. 16p. - Publication Year :
- 2017
-
Abstract
- This paper proposes parameter identification methods for multivariate pseudo-linear autoregressive systems. First, a multivariate generalized stochastic gradient (M-GSG) algorithm is presented as a comparison basis. In order to improve the parameter estimation accuracy, a multivariate multi-innovation generalized stochastic gradient (M-MI-GSG) algorithm and a filtering based multivariate generalized stochastic gradient (F-M-GSG) algorithm are presented by means of the multi-innovation identification theory and the data filtering technique. The simulation results confirm that the proposed algorithms are more effective than the M-GSG algorithm. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00160032
- Volume :
- 354
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Journal of the Franklin Institute
- Publication Type :
- Periodical
- Accession number :
- 120756538
- Full Text :
- https://doi.org/10.1016/j.jfranklin.2016.11.025