UM
Combination of activation functions in extreme learning machines for multivariate calibration
Jiangtao Peng1; Luoqing Li1; Yuan Yan Tang2
2013-01-15
Source PublicationChemometrics and Intelligent Laboratory Systems
ISSN0169-7439
Volume120Pages:53-58
Abstract

The key point in multivariate calibration is to build an accurate regression relationship between the predictors and responses. In this paper, we first use extreme learning machine (ELM) to build spectroscopy regression model. Then, we propose a combinational ELM (CELM) method in which the decision function is represented as a sum of a linear hidden-node output function (activation function) and a nonlinear hidden-node output function. As the output functions map the input spectral signal to linear and nonlinear feature spaces respectively, the proposed method can effectively describe the linear and nonlinear relations existed in spectroscopy regression by the CELM output weights vector which can be simply resolved by ridge least squares or alternative iterative regularization. The proposed method is compared, in terms of RMSEP, to PLS and ELM on simulated and real NIR data sets. Experimental results demonstrate the efficacy and effectiveness of the proposed method.

KeywordExtreme Learning Machine Linear And nonLinear Regression Multivariate Calibration Partial Least Squares
DOIhttps://doi.org/10.1016/j.chemolab.2012.11.004
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaAutomation & Control Systems ; Chemistry ; Computer Science ; Instruments & Instrumentation ; Mathematics
WOS SubjectAutomation & Control Systems ; Chemistry, Analytical ; Computer Science, Artificial Intelligence ; Instruments & Instrumentation ; Mathematics, Interdisciplinary Applications ; Statistics & Probability
WOS IDWOS:000314017000005
PublisherELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
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Cited Times [WOS]:21   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorLuoqing Li
Affiliation1.Faculty of Mathematics and Computer Science, Hubei University, Wuhan, 430062, China
2.Faculty of Science and Technology, University of Macau, Macao, China
Recommended Citation
GB/T 7714
Jiangtao Peng,Luoqing Li,Yuan Yan Tang. Combination of activation functions in extreme learning machines for multivariate calibration[J]. Chemometrics and Intelligent Laboratory Systems,2013,120:53-58.
APA Jiangtao Peng,Luoqing Li,&Yuan Yan Tang.(2013).Combination of activation functions in extreme learning machines for multivariate calibration.Chemometrics and Intelligent Laboratory Systems,120,53-58.
MLA Jiangtao Peng,et al."Combination of activation functions in extreme learning machines for multivariate calibration".Chemometrics and Intelligent Laboratory Systems 120(2013):53-58.
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