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Characterization of Non-linear Cellular Automata Model for Pattern Recognition

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Advances in Soft Computing — AFSS 2002 (AFSS 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2275))

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Abstract

This paper establishes the non-linear Cellular Automata (CA) as a powerful pattern recognizer. The special class of CA, referred to as GMACA (Generalized Multiple Attractor Cellular Automata), is employed for the design. The desired CA model, evolved through an efficient implementation of genetic algorithm, are found to be at the edge of chaos.

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References

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Author information

Authors and Affiliations

  1. Computer centre, IISWBM, 700073, Calcutta, India

    Niloy Ganguly

  2. Department of Computer Science & Technology, Bengal Engineering College (D U), 711103, Howrah, India

    Pradipta Maji, Arijit Das, Biplab K. Sikdar & P. Pal Chaudhuri

Authors
  1. Niloy Ganguly
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  2. Pradipta Maji
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  3. Arijit Das
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  4. Biplab K. Sikdar
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  5. P. Pal Chaudhuri
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Editor information

Editors and Affiliations

  1. Electronics and Communication Sciences Unit, Indian Statistical Institute, 203 B.T. Road, 700108, Calcutta, India

    Nikhil R. Pal

  2. Brain Science Institute, RIKEN, 2-1 Hirosawa, Wako, Japan

    Michio Sugeno

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© 2002 Springer-Verlag Berlin Heidelberg

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Ganguly, N., Maji, P., Das, A., Sikdar, B.K., Chaudhuri, P.P. (2002). Characterization of Non-linear Cellular Automata Model for Pattern Recognition. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_29

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  • DOI: https://doi.org/10.1007/3-540-45631-7_29

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43150-3

  • Online ISBN: 978-3-540-45631-5

  • eBook Packages: Springer Book Archive

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