Soft-Sensor of Water Quality Based on Integrated ELM with Meta-Learning
CONG Qiumei1,2,3, YUAN Mingzhe2,3, WANG Hong2,3,4, PANG Qiang2,3, WANG Jingyang2,3
1. School of Information and Control Engineering, Liaoning Shihua University, Fushun 113001, China;
2. Deptartment of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;
3. Key laboratory of networked control system, Chinese Academy of Sciences, Shenyang 110016, China;
4. Microcyber Inc. Shenyang 110179, China
A soft-sensor of water quality for wastewater treatment plants, which is based on an integrated model, is presented. The proposed soft-sensor aims to address the difficulty in using a single model to represent the characteristics of wastewater treatment processes with varying operating regimes.The soft-sensor is composed of three layers, in which a predictive sub-model based on FCM-ELMs are the bottom layer, adaptive weighted fusion method fusing predictive values of the sub-model are the middle layer, and a meta-learning mechanism based on information entropy updating fusion weights is the top layer. The meta-learning mechanism can track the dynamic trend of operating conditions of wasterwater treatment plants. The quick learning advantage of ELM results in the soft-sensor showing excellent real-time performance. The adaptive weighted fusion method and meta-learning mechanism improve the model generalization. Simulation results show that the integrated model for COD is more accurate than other models.
丛秋梅, 苑明哲, 王宏, 庞强, 王景杨. 基于元学习的污水水质集成软测量模型[J]. 信息与控制, 2014, 43(2): 248-252.
CONG Qiumei, YUAN Mingzhe, WANG Hong, PANG Qiang, WANG Jingyang. Soft-Sensor of Water Quality Based on Integrated ELM with Meta-Learning. Information and control, 2014, 43(2): 248-252.
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