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. pp. 1-17. ISSN 1049-8923 (In Press)
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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.
Creators(s): |
Xu, Huan, Ding, Feng, Gan, Min and Yang, Erfu ![]() | Item type: | Article |
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ID code: | 73615 |
Keywords: | nonlinear time series, parameter estimation, decomposition technique, multi-innovation identification, Manufactures, Biomedical Engineering, Chemical Engineering(all), Mechanical Engineering, Industrial and Manufacturing Engineering, Control and Systems Engineering, Electrical and Electronic Engineering, Aerospace Engineering |
Subjects: | Technology > Manufactures |
Department: | Faculty of Engineering > Design, Manufacture and Engineering Management |
Depositing user: | Pure Administrator |
Date deposited: | 14 Aug 2020 08:51 |
Last modified: | 05 Dec 2020 02:34 |
Related URLs: | |
URI: | https://strathprints.strath.ac.uk/id/eprint/73615 |
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