Title :
A comparison of two Hammerstein model identification algorithms
Author :
Gallman, Philip G.
Author_Institution :
University of Maryland, College Park, MD, USA
fDate :
2/1/1976 12:00:00 AM
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;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.1976.1101123
