DocumentCode :
468993
Title :
Wavelet neural network based calibration curve fitting in the quantitative assay
Author :
Gao, Yue-ming ; Du, Min
Author_Institution :
Fuzhou Univ., Fuzhou
Volume :
2
fYear :
2007
fDate :
2-4 Nov. 2007
Firstpage :
793
Lastpage :
797
Abstract :
In the present paper, the wavelet neural network (WNN) is adopted to fit the calibration curve during the quantitative assay of the gold immuno-chomatographic (GIC) strip of the serum marker-HCG (human chorionic gonadotrophin). Based on the wavelet transform theory, the structure and arithmetic of WNN are introduced. Considering how to improve the ability of extracting the signal feature of the network, the training energy function is modified using the information entropy of the hidden layer. The results indicate that the WNN has a better performance in the measure accuracy and reliability of fitting curve than the same size BP neural network.
Keywords :
calibration; curve fitting; drug delivery systems; feature extraction; neural nets; wavelet transforms; calibration curve fitting; gold immuno-chomatographic strip; human chorionic gonadotrophin; information entropy; quantitative assay; serum marker; signal feature extraction; wavelet neural network; Arithmetic; Calibration; Curve fitting; Data mining; Gold; Humans; Immune system; Neural networks; Strips; Wavelet transforms; Calibration curve; Gold immuno-chomatographic strip; Quantitative assay; Wavelet neural network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-1065-1
Electronic_ISBN :
978-1-4244-1066-8
Type :
conf
DOI :
10.1109/ICWAPR.2007.4420777
Filename :
4420777
Link To Document :
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