Selection of relevant variables for industrial process modeling by combining experimental data sensitivity and human knowledge

Selection of relevant variables from a high dimensional process operation setting space is a problem frequently encountered in industrial process modeling. This paper presents two global relevancy criteria, which permit to formalize and combine the sensitivity of experimental data and the conformity...

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Bibliographic Details
Published in:Engineering applications of artificial intelligence 2010-12, Vol.23 (8), p.1368-1379
Main Authors: Deng, Xiaoguang, Zeng, Xianyi, Vroman, Philippe, Koehl, Ludovic
Format: Article
Language:English
Subjects:
Artificial intelligence
Criteria
Data sensitivity
Expert systems
Feature selection
Fuzzy
Fuzzy logic
Fuzzy set theory
Fuzzy systems
Human
Industrial process modeling
Knowledge representation
Learning
Mathematical models
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Summary:Selection of relevant variables from a high dimensional process operation setting space is a problem frequently encountered in industrial process modeling. This paper presents two global relevancy criteria, which permit to formalize and combine the sensitivity of experimental data and the conformity of human knowledge using a liner and a fuzzy model, respectively. The performances of these relevancy criteria and some well-known selection methods are compared through artificial and real datasets. The result validates the outperformance of fuzzy global relevancy criterion, especially when the number of learning data is small and noisy.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2010.02.006