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Discovering Universal Polynomial Cellular Neural Networks through Genetic Algorithms

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Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 256))

Abstract

There are different benchmarks to test the capabilities of artificial neural networks. The Game of Life is an algorithm that has a very interesting mathematical characterization because can realize universal computing in the sense of a Turing machine. In this paper a new model of Polynomial Cellular Neural Networks that simulates a semi-totalistic automata is presented with the learning design to compute the templates. In this case, the rules of the semi-totalistic automata used, "play" the Game of Life. With the simulations presented we show that the PCNN can realize universal computing.

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Author information

Authors and Affiliations

  1. LIDETEA, POSGRADO E INVESTIGACION La Salle University, México, D.F.

    Eduardo Gomez-Ramirez & Enrique Haro Sedeño

  2. GRSI, Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Quatre Camins 2, 08022, Barcelona, Spain

    Giovanni Egidio Pazienza

Authors
  1. Eduardo Gomez-Ramirez
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  2. Enrique Haro Sedeño
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  3. Giovanni Egidio Pazienza
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Tijuana Institute of Technology, P.O. Box 4207, 91909, Chula Vista, CA, USA

    Patricia Melin

  2. Systems Research Institute, Newelska, Polish Academy of Sciences, 601-447, Warszawa, Poland

    Janusz Kacprzyk

  3. Dept. Electrical and Computer Engineering, University of Alberta, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

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Gomez-Ramirez, E., Sedeño, E.H., Pazienza, G.E. (2009). Discovering Universal Polynomial Cellular Neural Networks through Genetic Algorithms. In: Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition. Studies in Computational Intelligence, vol 256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04516-5_11

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  • DOI: https://doi.org/10.1007/978-3-642-04516-5_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04515-8

  • Online ISBN: 978-3-642-04516-5

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