Gradient-based and least-squares-based iterative algorithms for Hammerstein systems using the hierarchical identification principle

This study derives a least-squares-based iterative algorithm and a gradient-based iterative algorithm for Hammerstein systems using the decomposition-based hierarchical identification principle. The simulation results confirm that the proposed two algorithms can give satisfactory identification accu...

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Veröffentlicht in:IET control theory & applications Jg. 7; H. 2; S. 176 - 184
Hauptverfasser: Ding, Feng, Liu, Xinggao, Chu, Jian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Stevenage The Institution of Engineering and Technology 01.01.2013
John Wiley & Sons, Inc
Schlagworte:
Algorithms
Computer simulation
Control theory
Convergence
Decomposition
decomposition‐based hierarchical identification principle
fast convergence rates
gradient methods
gradient‐based iterative algorithm
Hammerstein systems
Iterative algorithms
iterative methods
least squares approximations
Least squares method
least‐squares‐based iterative algorithms
nonlinear control systems
parameter estimation
satisfactory identification accuracies
iterative methods
gradient-based iterative algorithm
least squares approximations
satisfactory identification accuracies
least-squares-based iterative algorithms
nonlinear control systems
parameter estimation
decomposition
gradient methods
decomposition-based hierarchical identification principle
Hammerstein systems
fast convergence rates
ISSN:1751-8644, 1751-8652
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Zusammenfassung:This study derives a least-squares-based iterative algorithm and a gradient-based iterative algorithm for Hammerstein systems using the decomposition-based hierarchical identification principle. The simulation results confirm that the proposed two algorithms can give satisfactory identification accuracies and the least-squares-based iterative algorithm has faster convergence rates than the gradient-based iterative algorithm.
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ISSN:1751-8644
1751-8652
DOI:10.1049/iet-cta.2012.0313