Fuzzy Recognition Intelligent System Based on Cash Character

Peng Wu; Ning Jun Ruan; Kai Xie

doi:10.4028/www.scientific.net/AMR.748.675

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 748Fuzzy Recognition Intelligent System Based on Cash...

Fuzzy Recognition Intelligent System Based on Cash Character

Abstract:

Actually as a question of pattern recognition, the research of cash recognition mainly focuses on three aspects: data acquisition, feature extraction and classifier design. In order to finish the real time recognition of Chinese cash, a fuzzy recognition intelligent system is presented in this paper. We use this arithmetic in the processing of information of cash and the recognition of the cash. Experiments has proved that this method can auto organize and auto study, and meet the need of complex system for the networks no linearity and high collateral.

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Advanced Materials Research (Volume 748)

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675-678

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https://doi.org/10.4028/www.scientific.net/AMR.748.675

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Online since:

August 2013

Authors:

Peng Wu, Ning Jun Ruan, Kai Xie

Keywords:

Cash Templates, Feature Extraction, Fuzzy Recognition, Similarity Matrix

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References

[1] Stefano Scanzio, Sandro Cumani, Roberto Gemello: Parallel implementation of Artificial Neural Network training for speech recognition, Pattern Recognition Letters, 31(11), August 2010, PP.1302-1309.

DOI: 10.1016/j.patrec.2010.02.003

Google Scholar

[2] Jianli Liu, Baoqi Zuo, Xianyi Zeng: Visual quality recognition of nonwovens using generalized Gaussian density model and robust Bayesian neural network, Neurocomputing, 74(17), October 2011, PP.2813-2823.

DOI: 10.1016/j.neucom.2011.03.048

Google Scholar

[3] Adnan Khashman: Modeling cognitive and emotional processes: A novel neural network architecture, Neural Networks, 23(10), December 2010, PP.1155-1163.

DOI: 10.1016/j.neunet.2010.07.004

Google Scholar

[4] Sankar K. Pal, Saroj K. Meher, Soumitra Dutta:Class-dependent rough-fuzzy granular space, dispersion index and classification, Pattern Recognition, 45(7), July 2012, PP.2690-2707.

DOI: 10.1016/j.patcog.2011.12.027

Google Scholar

[5] Yu Yinglin, Li Haizhou: Neural Networks and Signal Analysis, South China Univeersity of technology Press, (1995).

Google Scholar

[6] Sankar K Pal. and Sushmita: Multilayer Preceptron Fuzzy Nets and Classification, IEEE Trans. on neural Network, 3(5), sept.1992, PP, 683-697

Google Scholar

[7] Junli ,Yang Xingjun: Adaptive Pattern Recognition and Neural Networks, Higher Education Press, (1993).

Google Scholar

[8] Fu JIngsun:pattern Recognition Application, Peking University Press, (1992)

Google Scholar

[9] Jianye Sun: Learning algorithm and hidden node selection scheme for local coupled feedforward neural network classifier, Neurocomputing, 79(1), March 2012, PP.158-163.

DOI: 10.1016/j.neucom.2011.09.019

Google Scholar