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International Conference on Applied Informatics and Communication

ICAIC 2011: Applied Informatics and Communication pp 708–714Cite as

Research on Discretization Algorithm of Continuous Attribute Based on PCNN in a Bridge Erecting Machine Safety Monitoring System

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 225))

Abstract

PCNN (Pulse Coupled Neural Network) model is suitable for implementing clustering algorithm because of its unique neighbor coupled feature. This paper presented a new discretization algorithm of continuous attribute based on simplified PCNN model. Measured signals of the bridge erecting machine safety monitoring system are computed on this algorithm. Then decision rules are gained by using Rough Sets model to analyze the discretized result, among which the ratio of deterministic rules is 100 percent. Obviously it improves the certainty of decision and analysis better.

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References

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Author information

Authors and Affiliations

  1. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China

    Na Chen

  2. Shijiazhuang Tiedao University, Shijiazhuang, China

    Na Chen, Shaopu Yang, Cunzhi Pan & Erfu Guo

Authors
  1. Na Chen
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  2. Shaopu Yang
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  3. Cunzhi Pan
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  4. Erfu Guo
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Editor information

Editors and Affiliations

  1. Shenzhen University, Nanhai Ave. 3688, 518060, Shenzhen, Guangdong, China

    Dehuai Zeng

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© 2011 Springer-Verlag Berlin Heidelberg

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Cite this paper

Chen, N., Yang, S., Pan, C., Guo, E. (2011). Research on Discretization Algorithm of Continuous Attribute Based on PCNN in a Bridge Erecting Machine Safety Monitoring System. In: Zeng, D. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23220-6_90

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  • DOI: https://doi.org/10.1007/978-3-642-23220-6_90

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23219-0

  • Online ISBN: 978-3-642-23220-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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