Auxiliary model based identification methods.Part E: Least squares identification

doi:10.13878/j.cnki.jnuist.2016.05.001

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Home > Archive>Volume 8, Issue 5, 2016 >385-403. DOI:10.13878/j.cnki.jnuist.2016.05.001

Auxiliary model based identification methods.Part E: Least squares identification
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                        10.13878/j.cnki.jnuist.2016.05.001
                    
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                        DING FengDING Feng
School of Internet of Things Engineering, Jiangnan University, Wuxi 21412;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:

For input nonlinear finite impulse response systems,based on the auxiliary model identification idea,this paper studies the auxiliary model (AM) based least squares (LS) identification algorithms,the AM multi-innovation LS identification algorithms,the interval-varying AM LS identification algorithms,the interval-varying AM multi-innovation LS identification algorithms and the AM finite data window LS identification algorithms,including the weighted LS algorithms and the forgetting factor LS algorithms.

Key words:parameter estimation;recursive identification;least squares;key term separation;auxiliary model identification idea;multi-innovation identification theory;hierarchical identification principle;coupling identification concept;filtering identification idea;input nonlinear system;output nonlinear system

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DING Feng. Auxiliary model based identification methods. Part E: Least squares identification[J]. Journal of Nanjing University of Information Science & Technology,2016,8(5):385-403

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Received:October 04,2016
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Online: October 26,2016
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