Least-squares parameter estimation algorithm for a class of input nonlinear systems
type: Item
instance of: scholarly article
title: Least-squares parameter estimation algorithm for a class of input nonlinear systems
zbMATH Open document ID: 1251.62036
DOI: 10.1155/2012/684074
author: Weili Xiong
author: Wei Fan
author: Rui Ding
published in: Journal of Applied Mathematics
publication date: 2012-11-15T00:00:00Z
review text: Summary: We study 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.
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62F12
Mathematics Subject Classification ID: 65C60
zbMATH DE Number: 6106284
zbMATH Keywords: input nonlinear controlled autoregressive model
zbMATH Keywords: input nonlinear controlled autoregressive autoregressive moving average model
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/684074
OpenAlex ID: W1972203262
Wikidata QID: Q58907137
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DBLP publication ID: journals/jam/XiongFD12
description: scientific article; zbMATH DE number 6106284