DocumentCode
820352
Title
A comparison of two Hammerstein model identification algorithms
Author
Gallman, Philip G.
Author_Institution
University of Maryland, College Park, MD, USA
Volume
21
Issue
1
fYear
1976
fDate
2/1/1976 12:00:00 AM
Firstpage
124
Lastpage
126
Abstract
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.
Keywords
Discrete-time systems, nonlinear; Least-squares estimation; Nonlinear systems, discrete-time; Parameter estimation; Asymptotic stability; Control systems; Controllability; Differential equations; Eigenvalues and eigenfunctions; Iterative algorithms; Matrices; Numerical models; Parameter estimation; State feedback;
fLanguage
English
Journal_Title
Automatic Control, IEEE Transactions on
Publisher
ieee
ISSN
0018-9286
Type
jour
DOI
10.1109/TAC.1976.1101123
Filename
1101123
Link To Document