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Recursive weighted kernel regression for semi-supervised soft-sensing modeling of fed-batch processes
J. Taiwan Inst. Chem. Eng. (2011)
  • Jun Ji, Zhejiang University
  • Haiqing Wang
  • Kun Chen, Zhejiang University
  • Yi Liu
  • Neng Zhang
  • Junjie Yan, Zhejiang University

Soft-sensor techniques have been increasingly applied in chemical industry to establish an online monitor of unmeasured product indices. However, one intrinsic obstacle of soft-sensing modeling is insufficiency of labeled data while unlabeled data is abundant. In this work, a semi-supervised recursive weighted kernel regression (RWKR) method is proposed to model the soft-sensor by leveraging both labeled and unlabeled data. A novel weight strategy is presented to improve the prediction and its recursive algorithm is formulated, which adopts the incremental and decremental learning mechanism to update the soft-sensor model online and control the model complexity. Simulative soft-sensor for penicillin production process indicates that RWKR is superior to both relevance vector machine (RVM) and harmonic functions to model such fed-batch processes. Additionally, it sheds light on more competitive semi-supervised soft-sensing modeling approaches.

  • Recursive identification,
  • Soft-sensing online modeling,
  • Fed-batch processes,
  • Kernel regression,
  • Semi-supervised learning
Publication Date
Citation Information
Jun Ji, Haiqing Wang, Kun Chen, Yi Liu, et al.. "Recursive weighted kernel regression for semi-supervised soft-sensing modeling of fed-batch processes" J. Taiwan Inst. Chem. Eng. (2011)
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