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CLC number: TP273
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2014-01-15
Cited: 2
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Stochastic gradient algorithm for a dual-rate Box-Jenkins model based on auxiliary model and FIR model
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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",
volume="15",
number="2",
pages="147-152",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300072"
}
%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
TY - JOUR
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
Abstract: 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.
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