Auxiliary model based recursive and iterative least squares algorithm for autoregressive output error autoregressive systems

被引:3
作者
Jin, Qibing [1 ]
Cao, Liting [1 ]
Yang, Ruigeng [1 ]
Wang, Qi [1 ]
Wang, Zhu [1 ]
机构
[1] Beijing Univ Chem Technol, Inst Automat, Beijing 100029, Peoples R China
基金
中国国家自然科学基金; 国家教育部博士点专项基金资助;
关键词
Least squares; Auxiliary model; Recursive method; Iterative method; Autoregressive output error autoregressive (AR-DEAR) model; MOVING AVERAGE SYSTEMS; PARAMETER-ESTIMATION; IDENTIFICATION; DECOMPOSITION;
D O I
10.1016/j.apm.2015.02.038
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper considers the stochastic systems described by the autoregressive output error autoregressive (AR-OEAR) models. The AR-OEAR model is a special output error model which has an extra autoregressive term of the output signal. Basing on the auxiliary model idea, the recursive method and the iterative principle, the auxiliary model based recursive and iterative least squares algorithms are proposed to estimate the parameters of the autoregressive output error autoregressive system which contains the autoregressive output error system model and the autoregressive noise model, respectively. The simulation results indicate that the proposed algorithms can work well. (C) 2015 Elsevier Inc. All rights reserved.
引用
收藏
页码:7008 / 7016
页数:9
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