Modified stochastic gradient estimation algorithms for Box-Jenkins model based on auxiliary model
- 10.2991/icmmita-16.2016.231How to use a DOI?
- Parameter estimation; Stochastic gradient; Auxiliary model; Box-Jenkins model; Convergence rate
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.
- © 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/).
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 - 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 -