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Interpretation of Rough Neural Networks as Emergent Model

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2639))

Abstract

The need for more effective methods to generate and maintain global nonfunctional properties suggests an approach analogous to those of natural processes in generating emergent properties. Emergent model allows the constraints of the task to be represented more naturally and permits only pertinent task specific knowledge to emerge in the course of solving the problem. The paper describes some basics of emergent phenomena and its implementation in the rough hybrid systems.

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References

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Author information

Authors and Affiliations

  1. Toin University of Yokohama, Japan

    Yasser Hassan & Eiichiro Tazaki

Authors
  1. Yasser Hassan
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  2. Eiichiro Tazaki
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Editor information

Editors and Affiliations

  1. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P.R. China

    Guoyin Wang

  2. Department of Computer Science, Nanchang University, Nanchang, 330029, P.R. China

    Qing Liu

  3. Department of Computer Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada

    Yiyu Yao

  4. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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© 2003 Springer-Verlag Berlin Heidelberg

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Hassan, Y., Tazaki, E. (2003). Interpretation of Rough Neural Networks as Emergent Model. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_31

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  • DOI: https://doi.org/10.1007/3-540-39205-X_31

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-14040-5

  • Online ISBN: 978-3-540-39205-7

  • eBook Packages: Springer Book Archive

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