Model Optimization of Air Quality with M-ELM
- 10.2991/icmmita-16.2016.11How to use a DOI?
- Extreme learning machine; M-estimator; Model performance; Air quality model
The extreme learning machine (ELM), which was originally proposed for "generalized" single-hidden layer feedforward neural networks (SLFNs), provides efficient unified learning solutions for the applications of clustering, regression, and classification. But when the training data have been contaminated, ELM can't guarantee the model accuracy. A novel hybrid way called M-ELM is proposed to adjust the output matrix of ELM model, this way combined M-estimator with ELM to reduce the noise influence. Experimental results on UCI (University of California at Irvine ) datasets and air quality detection indicate that M-ELM performs competitively good, it can be used on design of air cleaner.
- © 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 - Jianxiong Ye AU - Wenzhen Zhou AU - Zhigang Li AU - Jinlan Zhou PY - 2017/01 DA - 2017/01 TI - Model Optimization of Air Quality with M-ELM BT - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications PB - Atlantis Press SP - 56 EP - 59 SN - 2352-538X UR - https://doi.org/10.2991/icmmita-16.2016.11 DO - 10.2991/icmmita-16.2016.11 ID - Ye2017/01 ER -