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A Neural Network Model for Pattern Recognition

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2774))

Abstract

We have developed a small scale four-layered neural network (NN) model for simple character recognition, which can recognize the patterns transformed by affine conversion. It learns by backpropagation to obtain the characteristics of the simple cell and the complex cell in the visual cortex. In this study 24 patterns are presented as input patterns. An input pattern is divided into 64 local patterns and connected with the 1st hidden layer as in the visual cortex. The proposed NN model has good performance of the feature extraction in first layers.

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Author information

Authors and Affiliations

  1. Faculty of Engineering, The Univ. of Tokushima, 2-1, Minamijosanjima, Tokushima, 770-8506, Japan

    Hirohito Shintani & Yohsuke Kinouchi

  2. School of Health Sciences, The Univ. of Tokushima, 3-18-15, Kuramoto, Tokushima, 770-8509, Japan

    Hirofumi Nagashino & Masatake Akutagawa

Authors
  1. Hirohito Shintani
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  2. Hirofumi Nagashino
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  3. Masatake Akutagawa
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  4. Yohsuke Kinouchi
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Editor information

Editors and Affiliations

  1. Computing Laboratory, Oxford University, Parks Road, OXI 3QD, Oxford, United Kingdom

    Vasile Palade

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. Knowledge-Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, SA 5095, Adelaide, Australia

    Lakhmi Jain

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© 2003 Springer-Verlag Berlin Heidelberg

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Shintani, H., Nagashino, H., Akutagawa, M., Kinouchi, Y. (2003). A Neural Network Model for Pattern Recognition. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_111

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  • DOI: https://doi.org/10.1007/978-3-540-45226-3_111

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40804-8

  • Online ISBN: 978-3-540-45226-3

  • eBook Packages: Springer Book Archive

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