Recursive Least Squares Parameter Estimation for a Class of Output Nonlinear Systems Based on the Model Decomposition
| Autor: | Qijia Chen, Feng Ding, Xuehai Wang, Xiao Yongsong |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Recursive least squares filter
0209 industrial biotechnology Mathematical optimization Class (set theory) Estimation theory Applied Mathematics 020206 networking & telecommunications 02 engineering and technology Non-linear iterative partial least squares Least squares Nonlinear system 020901 industrial engineering & automation Non-linear least squares Signal Processing 0202 electrical engineering electronic engineering information engineering Model decomposition Algorithm Mathematics |
| Zdroj: | Circuits, Systems, and Signal Processing. 35:3323-3338 |
| ISSN: | 1531-5878 0278-081X |
| DOI: | 10.1007/s00034-015-0190-6 |
| Popis: | In this paper, we study the parameter estimation problem of a class of output nonlinear systems and propose a recursive least squares (RLS) algorithm for estimating the parameters of the nonlinear systems based on the model decomposition. The proposed algorithm has lower computational cost than the existing over-parameterization model-based RLS algorithm. The simulation results indicate that the proposed algorithm can effectively estimate the parameters of the nonlinear systems. |
| Databáze: | OpenAIRE |
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