Model Optimization of Air Quality with M-ELM

DOI

10.2991/icmmita-16.2016.11How to use a DOI?

Keywords

Extreme learning machine; M-estimator; Model performance; Air quality model

Abstract

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.

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  - 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  -