NDLI: Improvement of Prediction Ability of Multicomponent Regression Model

Content Provider	IEEE Xplore Digital Library
Author	Gao, Ling Ren, Shouxin
Copyright Year	2008
Abstract	A novel method named wavelet packet transform based Elman recurrent neural network (WPTERNN) was proposed for simultaneous determination of Co (II), Zn (II) and Cu (II) by combining wavelet packet denoising with Elman recurrent neural network. Wavelet packet representations of signals provided a local time–frequency description, thus in the wavelet packet domain, the quality of the noise removal can be improved. Elman recurrent network was applied for non-linear multivariate calibration. In this case, by trials, wavelet function, decomposition level and numbers of hidden nodes for WPTERNN method were selected as Daubechies 2, 3 and 9 respectively. A program PWPTERNN was designed to perform simultaneous determination of Co (II), Zn (II) and Cu (II). The relative standard errors of prediction (RSEP) for all components with WPTERNN, Elman recurrent neural network (ERNN) and partial least squares (PLS), principal component regression (PCR) and Fourier transform based PCR (FTPCR) were 6.7, 14.7, 9.2, 25.6 and 25.2 % respectively. Experimental results demonstrated that the WPTERRN method had the best performance among the five methods.
Starting Page	102
Ending Page	106
File Size	411793
Page Count	5
File Format	PDF
ISBN	9780769531199
DOI	10.1109/CISP.2008.219
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Recurrent neural networks Wavelet domain Fourier transforms Noise reduction wavelet packet transform Predictive models Calibration ERNN Zinc Least squares methods multicomponent regression Wavelet packets
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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