Title :
On least squares algorithms for system parameter identification
Author_Institution :
University of California, Davis, CA, USA
fDate :
2/1/1976 12:00:00 AM
Abstract :
A new least squares solution for obtaining asymptotically unbiased and consistent estimates of unknown parameters in noisy linear systems is presented. The proposed algorithms are in many ways more advantageous than generalized least squares algorithm. Extensions to on-line and multivariable problems can be easily implemented. Examples are given to illustrate the performance of these new algorithms.
Keywords :
Least-squares estimation; Linear systems, stochastic discrete-time; Parameter identification; Additive noise; Automatic control; Equations; Filtering; Least squares approximation; Least squares methods; Linear systems; Parameter estimation; System identification; Yield estimation;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.1976.1101132
