Output prediction of CMF based on improved hybrid genetic algorithm and support vector machine

DOI

10.2991/iccte-16.2016.149How to use a DOI?

Keywords

continuous micro-filtration;support vector machine;accelerating genetic and simulated annealing algorithm;BP neural network;membrane flux

Abstract

Through improved select tactics and genetic operators, the accelerating genetic algorithm (AGA) and simulated annealing algorithm (SA) were combined to form a new algorithm called accelerating genetic and simulated annealing algorithm (AGSA). A modified method to develop the flow rate prediction model of the continuous micro-filtration (CMF) system was proposed based on improved hybrid genetic algorithm and support vector machine (SVM). A new self-adapting optimized algorithm was formed and applied to the SVM parameters. The hybrid genetic algorithm was utilized to perform variable selection, and SVM was employed to construct prediction models. The prediction models were verified by a flow rate experiment in a pilot-scale continuous micro-filtration system. Results showed that the proposed model can reveal the rule of flow rate variation in CMF. It produced a small error and exhibited strong correlation (R2=0.91, MAE=0.0132, SSE=0.0055, RMSE=0.0155) between predicted and measured values. This result reveals that the model has strong predictability. According to the leave-one-out cross validation of training samples, the model also shows good robustness (R2=0.89, MAE=0.0164, SSE=0.0073, RMSE=0.0178). The model developed by AGSA-SVM was compared with the model constructed by a BP neural network. The former exhibited optimal predictive capability and robustness in the comparison and is thus more suitable for the flow rate prediction of CMF.

Copyright

© 2016, 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  - Danyu Xu
AU  - Yan Shi
AU  - Yangyang You
AU  - Yunxia Duan
AU  - Ying Hou
PY  - 2016/01
DA  - 2016/01
TI  - Output prediction of CMF based on improved hybrid genetic algorithm and support vector machine
BT  - Proceedings of the 2016 International Conference on Civil, Transportation and Environment
PB  - Atlantis Press
SP  - 862
EP  - 871
SN  - 2352-5401
UR  - https://doi.org/10.2991/iccte-16.2016.149
DO  - 10.2991/iccte-16.2016.149
ID  - Xu2016/01
ER  -