Multi-innovation stochastic gradient parameter estimation for input nonlinear controlled autoregressive models

Multi-innovation stochastic gradient parameter estimation for input nonlinear controlled autoregressive models

This paper proposes a multi-innovation stochastic gradient (MISG) parameter estimation algorithm for an input nonlinear controlled autoregressive (IN-CAR) model, i.e., a Hammerstein nonlinear CAR system, by expanding the innovation length. The analysis and simulation results indicate that the propos...

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Veröffentlicht in:International journal of control, automation, and systems 2012, Automation, and Systems, 10(3), , pp.639-643
Hauptverfasser: Xiao, Yongsong, Song, Guanglei, Liao, Yuwu, Ding, Ruifeng
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Computer simulation
Control
Control systems
Engineering
Estimates
Estimating techniques
Identification
Mathematical models
Mechatronics
Nonlinearity
Parameter estimation
Process controls
Recursion theory
Regression analysis
Robotics
Stochastic models
Stochasticity
Studies
Technical Notes and Correspondence
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Beschreibung
Zusammenfassung:This paper proposes a multi-innovation stochastic gradient (MISG) parameter estimation algorithm for an input nonlinear controlled autoregressive (IN-CAR) model, i.e., a Hammerstein nonlinear CAR system, by expanding the innovation length. The analysis and simulation results indicate that the proposed MISG algorithm can generate more accurate parameter estimates for IN-CAR systems compared with the stochastic gradient algorithm.
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-012-0322-8