Decomposition based recursive least squares parameter estimation for input nonlinear equation-error systems

This paper developed a decomposition based recursive least square algorithm for estimating the parameters of an input nonlinear equation-error system, where the nonlinear system was parameterized as a bilinear-parameter system and decomposed it into two subsystems whose parameters were cross-estimat...

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Veröffentlicht in:Chinese Control Conference S. 2161 - 2165
Hauptverfasser: Chen Huibo, Fan Jiangbo
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Technical Committee on Control Theory, CAA 01.07.2017
Schlagworte:
Computational modeling
Estimation error
Least mean square methods
Least squares
Mathematical model
model decomposition
Nonlinear systems
Parameter estimation
ISSN:1934-1768
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Zusammenfassung:This paper developed a decomposition based recursive least square algorithm for estimating the parameters of an input nonlinear equation-error system, where the nonlinear system was parameterized as a bilinear-parameter system and decomposed it into two subsystems whose parameters were cross-estimated by the least squares methods. The proposed algorithm estimated much less parameters than the over-parameterization identification methods. Simulation results confirm the effectiveness of the proposed algorithm.
ISSN:1934-1768
DOI:10.23919/ChiCC.2017.8027676