Cascade Feature Selection and ELM for automatic fault diagnosis of the Tennessee Eastman process

Author:

de Assis Boldt FranciscoORCID,Rauber Thomas W.ORCID,Varejão Flávio M.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference39 articles.

1. Heterogeneous feature models and feature selection applied to bearing fault diagnosis;Rauber;IEEE Trans. Ind. Electron.,2015

2. An introduction to variable and feature selection;Guyon;J. Mach. Learn. Res.,2003

3. Rapid object detection using a boosted cascade of simple features;Viola,2001

4. Extreme learning machine: theory and applications;Huang;Neurocomputing,2006

5. Efficient leave-one-out cross-validation-based regularized extreme learning machine;Shao;Neurocomputing,2016

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