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Exploring the New Application of Morphological Neural Networks

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Intelligent Computing Theories and Application (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10361))

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Abstract

The traditional artificial neural networks can simulate the psychological phenomenon of “implicit learning”, but can’t simulate the cognitive phenomenon of “one-trial learning”. In this paper we took advantage of morphological associative memory networks to realize the simulation of “one-trial learning” for the first time. Theoretical analysis and simulation experiments show that the method of morphological associative memory networks is a higher effective machine learning method, and can very well simulate the cognitive phenomenon of “one-trial learning”, therefore, it will provide a new experimental tool for the study of intelligent science and cognitive science.

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Acknowledgement

This work was supported in part by the science and technology research project of Zhengzhou city (Grant No. 153PKJGG153).

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Authors and Affiliations

  1. School of Information Engineering, Zhengzhou University of Industrial Technology, Zhengzhou, 451150, China

    Bin Sun & Naiqin Feng

  2. College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China

    Naiqin Feng

Authors
  1. Bin Sun
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  2. Naiqin Feng
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Corresponding author

Correspondence to Bin Sun .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Politecnico di Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology, Kanpur, India

    Phalguni Gupta

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Sun, B., Feng, N. (2017). Exploring the New Application of Morphological Neural Networks. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-63309-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63308-4

  • Online ISBN: 978-3-319-63309-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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