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

DOI

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 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  - 10.2991/icmmita-16.2016.231
ID  - Feng2017/01
ER  -