Partially Coupled Stochastic Gradient Identification Methods for Non-Uniformly Sampled Systems

被引：264

作者：

Ding, Feng ^{[1
,2
]}

Liu, Guangjun ^{[3
]}

Liu, Xiaoping Peter ^{[4
]}

机构：

[1] Jiangnan Univ, Control Sci & Engn Res Ctr, Wuxi, Peoples R China

[2] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi, Peoples R China

[3] Ryerson Univ, Dept Aerosp Engn, Toronto, ON M5B 2K3, Canada

[4] Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2010年 / 55卷 / 08期

基金：

中国国家自然科学基金; 加拿大自然科学与工程研究理事会;

关键词：

Discretization; multirate systems; non-uniform sampling; parameter estimation; recursive identification; stochastic gradient; DUAL-RATE SYSTEMS; GENERALIZED PREDICTIVE CONTROL; PARAMETER-ESTIMATION; MULTIRATE SYSTEMS; AUXILIARY MODEL; OUTPUT ESTIMATION; STATE; BIOREACTOR;

D O I：

10.1109/TAC.2010.2050713

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 technical note addresses identification problems of non-uniformly sampled systems. For the input-output representation of non-uniform discrete-time systems, a partially coupled stochastic gradient (C-SG) algorithm is proposed to estimate the model parameters with high computational efficiency compared with the standard stochastic gradient (SG) algorithm. The analysis indicates that the partially C-SG algorithm can give more accurate parameter estimates than the SG algorithm. The parameter estimates obtained using the partially C-SG algorithm converge to their true values as the data length approaches infinity.

引用

页码：1976 / 1981

页数：6

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