NDLI: A nonlinear PLS modeling method based on extreme learning machine

Content Provider	IEEE Xplore Digital Library
Author	Chunxia Wang Jing Hu Chenglin Wen
Copyright Year	2015
Description	Author affiliation: Sch. of Autom., Hangzhou Dianzi Univ., Hangzhou, China (Chunxia Wang; Chenglin Wen) \|\| Dept. of Control Sci. & Control Eng., Zhejiang Univ., Hangzhou, China (Jing Hu)
Abstract	This paper proposes a nonlinear PLS modeling method. The extreme learning machine (ELM) is embedded in the process of linear PLS modeling. Thus the linear PLS modeling is transformed into the nonlinear frame which can deal with nonlinear data. The multi-input-multi-output (MIMO) nonlinear modeling task is decomposed into two parts: the external linear modeling and inner univariate nonlinear modeling problems. The linear PLS method is used to establish the external model, while the extreme learning machine is used to capture the inner nonlinear model. Compared to the standard PLS method, the method in this paper has the potential of modeling any continuous nonlinear relationship and has better robust properties. And it is less time-consuming than other neural networks PLS (NNPLS) methods. Because extreme learning machine can capture the inner nonlinearity of data, the proposed method has better prediction performance than linear PLS regression method. Simulation verifies the better prediction performance and the validity of the proposed method.
Starting Page	3507
Ending Page	3511
File Size	366739
Page Count	5
File Format	PDF
ISBN	9789881563897
ISSN	19341768
DOI	10.1109/ChiCC.2015.7260180
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Technical Committee on Control Theory, Chinese Association of Automation
Subject Keyword	Training nonlinear PLS Linear PLS model extreme learning machine Artificial neural networks Predictive models NNPLS Data models Mathematical model Load modeling
Content Type	Text
Resource Type	Article

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