On Reliability of Paper Currency Classifiers Using Neural Networks and PCA

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概要

• 論文の詳細を見る

• We propose an approach to increase the reliability of a neuro-classifier for paper currency recognition by using a principal component analysis (PCA) algorithm. The PCA is used to extract the main features of input data and reducing the data size. A learning vector quantization (LVQ) neural network is applied as the main classifier of the system. By defining a new algorithm for rating the reliability, we evaluate the reliability of the system for 1, 200 sample test data. The result shows that the average reliability measure is increased up to 99.6% when the number of PCA components as well as number of LVQ codebooks are taken properly.

• 社団法人 電気学会の論文
• 2003-07-01

著者

• Ahmadi Ali

  Department Of Computer And System Science Osaka Prefecture University

• Omatu S

  Division Of Computer And Systems Sciences Graduate School Of Engineering Osaka Prefecture University

• Omatu Sigeru

  Department Of Computer And System Sciences College Of Engineering Osaka Prefecture University

• KOSAKA Toshihisa

  Glory Ltd.

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