The research of least squares support vector machine optimized by particle swarm optimization algorithm in the simulation MBR prediction
Weiwei Li, Chunqing Li, Jingyun Nie, Tao Wang
Available Online June 2015.
- https://doi.org/10.2991/icecee-15.2015.195How to use a DOI?
- MBR; Membrane flux ; PCA; LSSVM; PSO
- This paper proposes an intelligent algorithm to predict the MBR membrane flux. The algorithm applies the least squares support vector machine (LS-SVM) to the research of MBR simulation prediction, optimize the penalty factor and kernel parameters of LS-SVM model by particle swarm optimization (PSO) for avoiding the blindness of artificial selection parameter. Due to the complexity and cross-cutting of the factors that affect MBR membrane fouling, first of all, we analyze the factors by principal component analysis (PCA), extract the important factors as the LS-SVM input layer, MBR membrane flux as output layer, and then create PSO-LSSVM prediction simulation model. In the end, we get predictive results with the model. By comparing the predicted results with experimental data, the algorithm has higher prediction accuracy for MBR membrane flux. To further verify the effectiveness of the algorithm, we also compare the model with BP neural network model, the results show that the prediction model of PSO-LSSVM has a higher prediction accuracy.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Weiwei Li AU - Chunqing Li AU - Jingyun Nie AU - Tao Wang PY - 2015/06 DA - 2015/06 TI - The research of least squares support vector machine optimized by particle swarm optimization algorithm in the simulation MBR prediction BT - 2015 2nd International Conference on Electrical, Computer Engineering and Electronics PB - Atlantis Press SP - 1030 EP - 1035 SN - 2352-538X UR - https://doi.org/10.2991/icecee-15.2015.195 DO - https://doi.org/10.2991/icecee-15.2015.195 ID - Li2015/06 ER -