Two-stage least squares based iterative estimation algorithm for CARARMA system modeling

被引：166
作者：
Ding, Feng [1 ,2 ]
机构：
[1] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China
[2] Jiangnan Univ, Control Sci & Engn Res Ctr, Wuxi 214122, Peoples R China
来源：
APPLIED MATHEMATICAL MODELLING | 2013年 / 37卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Least squares; Parameter estimation; Iterative identification; Recursive identification; PARAMETER-ESTIMATION ALGORITHM; IDENTIFICATION METHODS; PERFORMANCE ANALYSIS; STOCHASTIC-SYSTEMS; FORGETTING FACTOR; AUXILIARY MODEL; CONVERGENCE; DESIGN;
D O I：
10.1016/j.apm.2012.10.014
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
For stochastic systems described by the controlled autoregressive autoregressive moving average (CARARMA) models, a new-type two-stage least squares based iterative algorithm is proposed for identifying the system model parameters and the noise model parameters. The basic idea is based on the interactive estimation theory and to estimate the parameter vectors of the system model and the noise model, respectively. The simulation results indicate that the proposed algorithm is effective. (C) 2012 Elsevier Inc. All rights reserved.
引用
收藏
页码：4798 / 4808
页数：11
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