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Adaptive Local Weighted Kernel-based Regression for Online Modeling of Batch Processes
2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (2010)
  • Kun Chen, Zhejiang University
  • Haiqing Wang, Zhejiang University
  • Jun Ji, Zhejiang University
  • Zhihuan Song, Zhejiang University
Abstract

Fed-batch processes are inherently more difficult to model than continuous processes due to the variations under complex operation, drifting and small-sample condition. The classical kernel-based regression (KR) methods, e.g., least squares support vector regression (LSSVR), aim to achieve a universal generalization performance, which may be unsuitable for some local regions. Local LSSVR model which only uses the neighbors of the new sample helps improve the accuracy, but it leads a heavy computation load. Inspired by the idea of universal and local learning simultaneously, an adaptive local weighed kernel learning method (ALW-KR) is proposed here. In ALW-KR, adaptive weights are assigned to corresponding samples based on the similarity measurement, followed by a recursive updating to obtain local model. The proposed ALW-KR framework is applied to the prediction of biomass concentration in the penicillin fed-batch processes. The experimental results show that ALW-KR could predict the biomass concentration more accurate and robust to batch-to- batch variation than traditional KR methods.

Publication Date
Winter October 28, 2010
Citation Information
Kun Chen, Haiqing Wang, Jun Ji and Zhihuan Song. "Adaptive Local Weighted Kernel-based Regression for Online Modeling of Batch Processes" 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Vol. 3 (2010)
Available at: http://works.bepress.com/jun_ji/4/