Least squares based iterative parameter estimation algorithms for multivariate autoregressive moving average systems using the decomposition

被引：0
|
作者：
Ding Feng [1 ,2 ]
Wang Feifei [1 ]
Pan Jian [2 ]
机构：
[1] Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Peoples R China
[2] Hubei Univ Technol, Sch Elect & Elect Engn, Wuhan 430068, Peoples R China
来源：
PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016 | 2016年
关键词：
Parameter estimation; Iterative identication; Data ltering; Decomposition technique; Multivariate system; NONLINEAR-SYSTEMS; IDENTIFICATION METHOD; HAMMERSTEIN SYSTEMS; DYNAMICAL-SYSTEMS; NEWTON ITERATION; MODELS;
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 focuses on the parameter estimation problem of multivariate autoregressive moving average systems and develops a decomposition based least squares iterative identification algorithm using the data ltering. The basic idea is to transform the original system to a hierarchical identi cation model to decompose the hierarchical model into three subsystems and to identify each subsystem one by one. Compared with the least squares based iterative algorithm, the proposed decomposition algorithm requires less computational efforts. A simulation example is provided to test the proposed algorithm.
引用
收藏
页码：1981 / 1986
页数：6
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