Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications

Modified stochastic gradient estimation algorithms for Box-Jenkins model based on auxiliary model

Authors
Jianxia Feng
Corresponding Author
Jianxia Feng
Available Online January 2017.
DOI
https://doi.org/10.2991/icmmita-16.2016.231How to use a DOI?
Keywords
Parameter estimation; Stochastic gradient; Auxiliary model; Box-Jenkins model; Convergence rate
Abstract
An auxiliary model based stochastic gradient estimation algorithm in proposed in this paper. The unknown variables in the information vector can be estimated by using the auxiliary model. Then the unknown parameters can be estimated by the stochastic gradient algorithm. Furthermore, in order to increase the convergence rate, a modified stochastic gradient algorithm is also proposed. The simulation results indicate that the proposed algorithm has good performances.
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Proceedings
2016 4th International Conference on Machinery, Materials and Information Technology Applications
Part of series
Advances in Computer Science Research
Publication Date
January 2017
ISBN
978-94-6252-285-5
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmmita-16.2016.231How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jianxia Feng
PY  - 2017/01
DA  - 2017/01
TI  - Modified stochastic gradient estimation algorithms for Box-Jenkins model based on auxiliary model
BT  - 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.231
DO  - https://doi.org/10.2991/icmmita-16.2016.231
ID  - Feng2017/01
ER  -