Title :
Recognition for the Banknotes Grade Based on CPN
Author :
Sun, Baiqing ; Li, Jilu
Author_Institution :
Sch. of Electr. Eng., Shen yang Univ. of Technol., Shen yang
Abstract :
The counter propagation networks (CPN) is used to improve the accuracy of the grade of banknotes recognition. First, self-organizing map (SOM) is used to cluster banknotes data into regions based on the different feature of banknotes; second, the principal component analysis (PCA) is performed in each region to extract the main principal features of banknotes data; finally, the CPN is employed as the main classifier to identify the banknotes of different grades. The recognition effects of CPN and BP are compared in this paper. The results show that the reliability and speed of CPN are greatly better than that of the BP. The experimentation shows that the CPN can primly solve the recognition problem of the grade of banknotes.
Keywords :
backpropagation; bank data processing; feature extraction; pattern classification; pattern clustering; principal component analysis; self-organising feature maps; backpropagation; banknote classifier; banknote data clustering; banknote grade recognition; counter propagation network; feature extraction; principal component analysis; self-organizing map; Computer science; Counting circuits; Data mining; Feature extraction; Higher order statistics; Information science; Neural networks; Principal component analysis; Software engineering; Sun;
Conference_Titel :
Computer Science and Software Engineering, 2008 International Conference on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-0-7695-3336-0
DOI :
10.1109/CSSE.2008.881
