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CLC number: TP273

On-line Access: 2014-01-29

Received: 2013-03-26

Revision Accepted: 2013-05-14

Crosschecked: 2014-01-15

Cited: 2

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Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.2 P.147-152


Stochastic gradient algorithm for a dual-rate Box-Jenkins model based on auxiliary model and FIR model

Author(s):  Jing Chen, Rui-feng Ding

Affiliation(s):  School of Science, Jiangnan University, Wuxi 214122, China; more

Corresponding email(s):   chenjing1981929@126.com

Key Words:  Parameter estimation, Auxiliary model, Dual-rate system, Stochastic gradient, Box-Jenkins model, FIR model

Jing Chen, Rui-feng Ding. Stochastic gradient algorithm for a dual-rate Box-Jenkins model based on auxiliary model and FIR model[J]. Journal of Zhejiang University Science C, 2014, 15(2): 147-152.

@article{title="Stochastic gradient algorithm for a dual-rate Box-Jenkins model based on auxiliary model and FIR model",
author="Jing Chen, Rui-feng Ding",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Stochastic gradient algorithm for a dual-rate Box-Jenkins model based on auxiliary model and FIR model
%A Jing Chen
%A Rui-feng Ding
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 2
%P 147-152
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300072

T1 - Stochastic gradient algorithm for a dual-rate Box-Jenkins model based on auxiliary model and FIR model
A1 - Jing Chen
A1 - Rui-feng Ding
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 2
SP - 147
EP - 152
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300072

Based on the work in Ding and Ding (2008), we develop a modified stochastic gradient (SG) parameter estimation algorithm for a dual-rate box-Jenkins model by using an auxiliary model. We simplify the complex dual-rate box-Jenkins model to two finite impulse response (FIR) models, present an auxiliary model to estimate the missing outputs and the unknown noise variables, and compute all the unknown parameters of the system with colored noises. Simulation results indicate that the proposed method is effective.


重要结论:1. 采用有限脉冲方法,将复杂的Box-Jenkins模型转化成两个简单的有限脉冲模型。2. 利用损失数据估计方法辨识出系统丢失的数据和未知的噪声向量。3. 利用辨识出的数据能计算出带有有色噪声干扰的原系统的参数。4. 不会造成待辨识参数维数增大。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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