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Dynamics of Incremental Learning by VSF-Network

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Artificial Neural Networks – ICANN 2009 (ICANN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5768))

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Abstract

In this paper, we report the dynamics of VSF-Network. VSF-Network is a neural network for the incremental learning and it is a hybrid neural network combining the chaos neural network with a hierarchical network. VSF-Network can find the unknown elements from input with clusters generated by the chaos neuron. We introduce new incremental learning model to explain the dynamics of VSF-Network in this paper. We show the result of analysis of the dynamics of VSF-Network. In the analysis, we focused on the connection weights between layers and neuron cluster generated by the chaotic behavior.

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Author information

Authors and Affiliations

  1. Harumi Center Building, The JSOL, Ltd, Tokyo, 2-5-24 Harumi, Chuo-ku, Japan

    Yoshitsugu Kakemoto

  2. The University of Tokyo, Tokyo, 7-3-1 Hongo, Bunkyo-ku, Japan

    Shinchi Nakasuka

Authors
  1. Yoshitsugu Kakemoto
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  2. Shinchi Nakasuka
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Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milano, Italy

    Cesare Alippi

  2. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Street, 1678, Nicosia, Cyprus

    Marios Polycarpou , Christos Panayiotou  & Georgios Ellinas ,  & 

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© 2009 Springer-Verlag Berlin Heidelberg

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Kakemoto, Y., Nakasuka, S. (2009). Dynamics of Incremental Learning by VSF-Network. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_71

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  • DOI: https://doi.org/10.1007/978-3-642-04274-4_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04273-7

  • Online ISBN: 978-3-642-04274-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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