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Nonlinear process monitoring using kernel dictionary learning with application to aluminum electrolysis process

  • Published:2019-08 Issue: Volume:89 Page:94-102
  • ISSN:0967-0661
  • Container-title:Control Engineering Practice
  • language:en
  • Short-container-title:Control Engineering Practice

Author:

Huang Keke,Wen Haofei,Ji Hongquan,Cen Lihui,Chen Xiaofang,Yang Chunhua

Funder

National Natural Science Foundation of China

Foundation for Innovative Research Groups of the National Natural Science Foundation of China

Innovation-Driven Plan in Central South University, China

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Reference46 articles.

1. Subspace method aided data-driven design of fault detection and isolation systems;Ding;Journal of Process Control,2009

2. Nonlinear principal component analysis—based on principal curves and neural networks;Dong;Computers & Chemical Engineering,1996

3. Image denoising via sparse and redundant representations over learned dictionaries;Elad;IEEE Transactions on Image Processing,2006

4. Nonlinear process monitoring based on linear subspace and Bayesian inference;Ge;Journal of Process Control,2010

5. Projective dictionary pair learning for pattern classification;Gu,2014

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