Abstract
This paper concentrates on the recursive identification algorithms for the exponential autoregressive model with moving average noise. Using the decomposition technique, we transform the original identification model into a linear and nonlinear sub-identification model and derive a two-stage least squares extended stochastic gradient algorithm. In order to improve the parameter estimation accuracy, we employ the multi-innovation identification theory and develop a two-stage least squares multi-innovation extended stochastic gradient algorithm. A simulation example is provided to test the effectiveness of the proposed algorithms.
Original language | English |
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Pages (from-to) | 7766-7782 |
Number of pages | 17 |
Journal | International Journal of Robust and Nonlinear Control |
Volume | 30 |
Issue number | 17 |
DOIs | |
Publication status | Published - 25 Nov 2020 |
Keywords
- nonlinear time series
- parameter estimation
- decomposition technique
- multi-innovation identification