Journal of Applied Mathematics

Least-Squares Parameter Estimation Algorithm for a Class of Input Nonlinear Systems

Weili Xiong, Wei Fan, and Rui Ding

Full-text: Open access

Abstract

This paper studies least-squares parameter estimation algorithms for input nonlinear systems, including the input nonlinear controlled autoregressive (IN-CAR) model and the input nonlinear controlled autoregressive autoregressive moving average (IN-CARARMA) model. The basic idea is to obtain linear-in-parameters models by overparameterizing such nonlinear systems and to use the least-squares algorithm to estimate the unknown parameter vectors. It is proved that the parameter estimates consistently converge to their true values under the persistent excitation condition. A simulation example is provided.

Article information

Source
J. Appl. Math., Volume 2012 (2012), Article ID 684074, 14 pages.

Dates
First available in Project Euclid: 14 December 2012

Permanent link to this document
https://projecteuclid.org/euclid.jam/1355495245

Digital Object Identifier
doi:10.1155/2012/684074

Mathematical Reviews number (MathSciNet)
MR2959986

Zentralblatt MATH identifier
1251.62036

Citation

Xiong, Weili; Fan, Wei; Ding, Rui. Least-Squares Parameter Estimation Algorithm for a Class of Input Nonlinear Systems. J. Appl. Math. 2012 (2012), Article ID 684074, 14 pages. doi:10.1155/2012/684074. https://projecteuclid.org/euclid.jam/1355495245


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