Auxiliary model identification methods.Part A: Autoregressive output-error systems

doi:10.13878/j.cnki.jnuist.2016.01.001

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Auxiliary model identification methods.Part A: Autoregressive output-error systems
DOI:
                        10.13878/j.cnki.jnuist.2016.01.001
                    
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Author:
                        DING FengDING Feng
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122;Control Science and Engineering Research Center, Jiangnan University, Wuxi 214122;Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122
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Abstract:

This paper presents an auxiliary model (AM) based stochastic gradient (SG) algorithm,an AM multi-innovation SG algorithm and an AM recursive least squares algorithm for autoregressive output-error systems and presents a filtering based AM generalized extended SG algorithm,a filtering based AM multi-innovation generalized extended SG algorithm and a filtering based AM recursive generalized extended least squares algorithm for autoregressive output-error autoregressive moving average (AR-OEARMA) systems,namely autoregressive Box-Jenkins systems.

Key words:parameter estimation;recursive identification;gradient search;least squares;filtering;decomposition;auxiliary model identification idea;multi-innovation identification theory;hierarchical identification principle;equation-error system;output-error system;linear system

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DING Feng. Auxiliary model identification methods. Part A: Autoregressive output-error systems[J]. Journal of Nanjing University of Information Science & Technology,2016,8(1):1-22

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Received:November 21,2015
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Online: February 24,2016
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