DocumentCode
1859035
Title
A method of pattern classification for faint signals
Author
Zuojin, Li ; Liukui, Chen ; Ying, Wu ; Yi, Xiang
Author_Institution
Chongqing Univ. of Sci. & Technol., Chongqing, China
Volume
3
fYear
2011
fDate
13-15 May 2011
Firstpage
473
Lastpage
476
Abstract
This paper proposes a faint signal processing approach combining AR model and BP neural network (NN), by which the faint signal is fitted with AR model, whose coefficient served as signal eigenvector, and then sent into a three-tier BP NN for training and recognition classification. Classification tests on human pulse signals between drug users and non-users show that this approach is characterized in high speed and high recognition rate.
Keywords
backpropagation; eigenvalues and eigenfunctions; neural nets; pattern classification; signal classification; AR model; faint signal processing approach; human pulse signals; pattern classification; recognition classification; signal eigenvector; three-tier BP neural network; training classification; Artificial neural networks; Correlation; Data models; Drugs; Mathematical model; Time series analysis; Training; AR model; LM algorithm; orders; pulse faint signal;
fLanguage
English
Publisher
ieee
Conference_Titel
Business Management and Electronic Information (BMEI), 2011 International Conference on
Conference_Location
Guangzhou
Print_ISBN
978-1-61284-108-3
Type
conf
DOI
10.1109/ICBMEI.2011.5920497
Filename
5920497
Link To Document