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Parameter estimation for nonlinear systems by using the data filtering and the multi-innovation identification theory.
- Source :
- International Journal of Computer Mathematics; Nov2016, Vol. 93 Issue 11, p1869-1885, 17p
- Publication Year :
- 2016
-
Abstract
- For Hammerstein output-error autoregressive systems, a decomposition based multi-innovation stochastic gradient (D-MISG) identification algorithm and a data filtering based multi-innovation stochastic gradient (F-MISG) identification algorithm are derived by means of the key-term separation principle and the multi-innovation identification theory. The D-MISG algorithm uses the decomposition technique to transform a Hammerstein system into two subsystems and requires less computational cost, and the F-MISG algorithm uses a linear filter to filter the input-output data and has a higher estimation accuracy for larger innovation lengths. The simulation results show that the proposed two algorithm can give satisfactory parameter estimates. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 00207160
- Volume :
- 93
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- International Journal of Computer Mathematics
- Publication Type :
- Academic Journal
- Accession number :
- 117632569
- Full Text :
- https://doi.org/10.1080/00207160.2015.1077949