A comparison of two Hammerstein model identification algorithms

A comparison of two Hammerstein model identification algorithms

Two algorithms for least-squares estimation of parameters of a Hammerstein model are compared. Numerical examples demonstrate that the iterative method of Narendra and Gallman produces significantly smaller parameter covariance and slightly smaller rms error than the noniterative method of Chang and...

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Veröffentlicht in:IEEE transactions on automatic control 1976-02, Vol.21 (1), p.124-126
1. Verfasser: Gallman, P.
Format: Artikel
Sprache:eng
Schlagworte:
Asymptotic stability
Control systems
Controllability
Differential equations
Eigenvalues and eigenfunctions
Iterative algorithms
Matrices
Numerical models
Parameter estimation
State feedback
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Zusammenfassung:Two algorithms for least-squares estimation of parameters of a Hammerstein model are compared. Numerical examples demonstrate that the iterative method of Narendra and Gallman produces significantly smaller parameter covariance and slightly smaller rms error than the noniterative method of Chang and Luus, as expected from an analysis of the parameter estimators. In addition, the iterative algorithm is faster for high-order systems.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.1976.1101123