Soft sensor development for the key variables of complex chemical processes using a novel robust bagging nonlinear model integrating improved extreme learning machine with partial least square

Chemometrics and Intelligent Laboratory Systems

Volumn 151, Issue , 2016, Pages 78-88

  He, YanLin ^a,b   Geng, ZhiQiang ^a,b   Zhu, QunXiong ^a,b  

^a BEIJING UNIVERSITY OF CHEMICAL TECHNOLOGY   (China)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

Bagging; Extreme learning machine; Partial least square; Purified Terephthalic Acid Process; Soft sensor; Tennessee Eastman process

Indexed keywords

ACCURACY; ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; INFORMATION PROCESSING; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MEMBRANE STRUCTURE; NONLINEAR SYSTEM; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PURIFICATION; PURIFIED TEREPHTHALIC ACID PROCESS; SIMULATION; TENNESSEE EASTMAN PROCESS; VALIDATION PROCESS;

EID: 84952926812     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2015.12.010     Document Type: Article

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