Decomposition based iterative estimation algorithm for autoregressive moving average models

被引：0
|
作者：
Hu, Huiyi [1 ]
Ding, Ruifeng [2 ]
机构：
[1] Jiangnan Univ, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Peoples R China
[2] Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Peoples R China
来源：
PROCEEDINGS OF THE 31ST CHINESE CONTROL CONFERENCE | 2012年
基金：
中国国家自然科学基金;
关键词：
Parameter estimation; Least squares; Iterative method; ARMA model; SQUARES PARAMETER-ESTIMATION; STOCHASTIC GRADIENT ALGORITHMS; SYLVESTER MATRIX EQUATIONS; SELF-TUNING CONTROL; IDENTIFICATION METHODS; AUXILIARY MODEL; PERFORMANCE ANALYSIS; HIERARCHICAL IDENTIFICATION; OUTPUT ESTIMATION; FORGETTING FACTOR;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper discusses an iterative least squares algorithm for identifying the parameters of autoregressive moving average models using the matrix decomposition technique. The basic idea is to use the block matrix inversion lemma to avoid repeatedly computing the inverse of the involved data matrix at each iteration. The simulation results show that the proposed algorithm works well.
引用
收藏
页码：1932 / 1937
页数：6
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