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Finite Precision Extended Alternating Projection Neural Network (FPEAP)

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Bio-Inspired Computing and Applications (ICIC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 6840))

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Abstract

The paper studies finite precision Extended Alternating Projection Neural Network (FPEAP) and its related problems. An improved training method of FPEAP has been present after considering the finite precision influence on the training method of EAP. Then the mathematical relation among the factors influencing the association times has been studied. Finally simulation experiments have been designed and simulation results demonstrate validity of theoretical analyses.

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References

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Author information

Authors and Affiliations

  1. New Star Research Institute of Applied Technology, 451 Huangshan Road, Hefei, Anhui, China

    Yanfei Wang & Jingen Wang

Authors
  1. Yanfei Wang
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  2. Jingen Wang
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, No. 5, Dongfeng Road, Jinshui District, 450002, Zhengzhou, Henan, China

    Yong Gan

  3. School of Electrical, Computer & Telecommunications Engineering, University of Wollongong, 2522, P.O. Box, North Wollongong, NSW, Australia

    Prashan Premaratne

  4. School of Computer Science and Engineering, Inha University, 402-751, Inchon, Korea

    Kyungsook Han

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© 2012 Springer-Verlag Berlin Heidelberg

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Wang, Y., Wang, J. (2012). Finite Precision Extended Alternating Projection Neural Network (FPEAP). In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_4

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  • DOI: https://doi.org/10.1007/978-3-642-24553-4_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24552-7

  • Online ISBN: 978-3-642-24553-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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