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.
|Number of pages||17|
|Journal||International Journal of Robust and Nonlinear Control|
|Publication status||Accepted/In press - 10 Aug 2020|
- nonlinear time series
- parameter estimation
- decomposition technique
- multi-innovation identification
Xu, H., Ding, F., Gan, M., & Yang, E. (Accepted/In press). Two-stage recursive identification algorithms for a class of nonlinear time series models with colored noise. International Journal of Robust and Nonlinear Control , 1-17.