Abstract
To increase the reliability of currency classification, a classification method using neural networks with multi-pattern vectors is proposed in this paper. The data space of samples are divided into three blocks, then the latter are further divided into four sub-pattern vectors, and kernel principal component analysis is applied to extract features and assemble feature vectors to train LVQ neural network classifier. We draw the conclusion by testing new fifth edition RMB including four kinds of inputting directions of 1 Yuan, 5 Yuan, 10 Yuan and 20 Yuan RMB, up to 800 samples that PCA can compress data and decrease dimension of input vectors, extract the feature vectors effectively, thus the high-level reliability can be achieved by using the LVQ network classifier.
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Gou, H., Li, X., Li, X., Yi, J. (2011). A Reliable Classification Method for Paper Currency Based on LVQ Neural Network. In: Zhou, M., Tan, H. (eds) Advances in Computer Science and Education Applications. Communications in Computer and Information Science, vol 202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22456-0_35
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DOI: https://doi.org/10.1007/978-3-642-22456-0_35
Publisher Name: Springer, Berlin, Heidelberg
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