Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)

Application of Regularized Extreme Learning Machine Based on BIC Criterion and Genetic Algorithm in Iron Ore Price Forecasting

Authors
Futian Weng, Muzhou Hou, Tianle Zhang, Yunlei Yang, Zheng Wang, Hongli Sun, Hao Zhu, Jianshu Luo
Corresponding Author
Futian Weng
Available Online July 2018.
DOI
https://doi.org/10.2991/msam-18.2018.45How to use a DOI?
Keywords
BIC criterion; genetic algorithm; regularized extreme learning machine; iron ore price forecast
Abstract
Forecasting international iron ore is a well-known issue, BIC criterion is used to select the relevant variables of iron ore price. On the basis of the traditional extreme learning machine (ELM), the regular term is introduced to control the complexity of the model, and the genetic algorithm (GA) is used to regularize the extreme learning machine. The input-layer weight matrix and the hidden-layer threshold matrix of the (RE-ELM) model are optimized to establish a BIC-based genetic algorithm and a regularization extreme learning machine (BIC-GA-RELM) iron ore price prediction model to increase the performance of the RE-ELM model. The results show that BIC-GA-RELM model has achieved the state of art performance, then a new method is provided for iron ore price prediction.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Futian Weng
AU  - Muzhou Hou
AU  - Tianle Zhang
AU  - Yunlei Yang
AU  - Zheng Wang
AU  - Hongli Sun
AU  - Hao Zhu
AU  - Jianshu Luo
PY  - 2018/07
DA  - 2018/07
TI  - Application of Regularized Extreme Learning Machine Based on BIC Criterion and Genetic Algorithm in Iron Ore Price Forecasting
BT  - Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)
PB  - Atlantis Press
SP  - 212
EP  - 217
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-18.2018.45
DO  - https://doi.org/10.2991/msam-18.2018.45
ID  - Weng2018/07
ER  -