Two-stage recursive identification algorithms for a class of nonlinear time series models with colored noise

Xu, Huan and Ding, Feng and Gan, Min and Yang, Erfu (2020) Two-stage recursive identification algorithms for a class of nonlinear time series models with colored noise. International Journal of Robust and Nonlinear Control, 30 (17). pp. 7766-7782. ISSN 1049-8923 (https://doi.org/10.1002/rnc.5206)

[thumbnail of Xu-etal-IJRNC-2020-Two-stage-recursive-identification-algorithms-for-a-class-of-nonlinear-time-series]
Preview
 Text. Filename: Xu_etal_IJRNC_2020_Two_stage_recursive_identification_algorithms_for_a_class_of_nonlinear_time_series.pdf
Accepted Author Manuscript
Download (489kB)| Preview

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.

CORE (COnnecting REpositories)