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Enhancing Cellular Automata by an Embedded Generalized Multi-layer Perceptron

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Artificial Neural Networks: Biological Inspirations – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3696))

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Abstract

A hybrid approach combining Cellular Automata (CA) and Artificial Neural Networks (ANNs), capable of providing suitable dynamic simulations of some complex systems, is formalized and tested. The proposed method allows to incorporate in the CA transition function the available a priori knowledge of the interaction rules between the elementary system constituents. In order to effectively describe the remaining unknown local rules, an embedded ANN is exploited. The ANN component of the transition function is designed, on the basis of the available data about the emerging behavior of the system to be simulated, by an evolutionary strategy involving both the architecture and weights.

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Authors and Affiliations

  1. University of Calabria, Center of High-Performance Computing, Rende (CS), Italy

    Giuseppe A. Trunfio

Authors
  1. Giuseppe A. Trunfio
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Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015 HUT, Finland

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

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© 2005 Springer-Verlag Berlin Heidelberg

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Trunfio, G.A. (2005). Enhancing Cellular Automata by an Embedded Generalized Multi-layer Perceptron. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_54

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  • DOI: https://doi.org/10.1007/11550822_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28752-0

  • Online ISBN: 978-3-540-28754-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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