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Inverse Function Delayed Model for Optimization Problems

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3213))

Abstract

The ID model is a novel model derived from a macroscopic model that is attached to conventional network action, and the important character is what we can introduce negative resistance effect into. In this paper, we aim at the unstabilization of local minimum states, which is a big problem to solving optimization problems in a neural network, by the action of this negative resistance effect, and we show the good performance by numerical experiments.

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Author information

Authors and Affiliations

  1. Laboratory for Brainware Systems, Laboratory for Nanoelectronics and Spintronics, Research Institute of Electrical Communication Tohoku University, Sendai, 980-8577, Japan

    Yoshihiro Hayakawa, Tatsuaki Denda & Koji Nakajima

Authors
  1. Yoshihiro Hayakawa
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  2. Tatsuaki Denda
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  3. Koji Nakajima
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Editor information

Editors and Affiliations

  1. KES International, 2nd Floor, 145-157 St John Street, EC1V 4PY, London, United Kingdom

    Mircea Gh. Negoita

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2004 Springer-Verlag Berlin Heidelberg

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Hayakawa, Y., Denda, T., Nakajima, K. (2004). Inverse Function Delayed Model for Optimization Problems. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_132

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  • DOI: https://doi.org/10.1007/978-3-540-30132-5_132

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23318-3

  • Online ISBN: 978-3-540-30132-5

  • eBook Packages: Springer Book Archive

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