Iterative Parameter Estimation for a Class of Multivariable Systems Based on the Hierarchical Identification Principle and the Gradient Search

被引：45
|
作者：
Wang, Dongqing [1 ]
Ding, Rui [2 ]
Dong, Xinzhuang [1 ]
机构：
[1] Qingdao Univ, Coll Automat Engn, Qingdao 266071, Peoples R China
[2] Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Peoples R China
来源：
CIRCUITS SYSTEMS AND SIGNAL PROCESSING | 2012年 / 31卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Hierarchical identification; Multivariable systems; Stochastic gradient; Least squares; Iterative identification; SYLVESTER MATRIX EQUATIONS; STATE-SPACE MODELS; DUAL-RATE SYSTEMS; CHANNEL ESTIMATION; SAMPLED-DATA; ALGORITHMS;
D O I：
10.1007/s00034-012-9425-y
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
For a multivariable controlled autoregressive system with autoregressive noises, its corresponding identification model contains a parameter matrix and a parameter vector. This paper presents the hierarchical gradient-based iterative (HGI) algorithm to interactively estimate the parameter matrix and the parameter vector by using the hierarchical identification principle and the gradient search. The simulation results show that the HGI algorithm is effective.
引用
收藏
页码：2167 / 2177
页数：11
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