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Evolving Cellular Automata Based Associative Memory for Pattern Recognition

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High Performance Computing — HiPC 2001 (HiPC 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2228))

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Abstract

This paper reports a Cellular Automata (CA )model for pattern recognition.The special class of CA referred to as GMACA (Generalized Multiple Attractor Cellular Automata ),is employed to design the CA based associative memory for pattern recognition.The desired GMACA are evolved through the implementation of genetic algorithm (GA).An efficient scheme to ensure fast convergence of GA is also reported.Experimental results conform the fact that the GMACA based pattern recognizer is more powerful than the Hopfield network for memorizing arbitrary patterns.

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Author information

Authors and Affiliations

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

    Niloy Ganguly & P. Pal Chaudhuri

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

    Arijit Das, Pradipta Maji & Biplab K. Sikdar

Authors
  1. Niloy Ganguly
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  2. Arijit Das
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  3. Pradipta Maji
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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. Department of Mathematics and Computer Science, University of Paderborn, Fürstenallee 11, 33102, Paderborn, Germany

    Burkhard Monien

  2. Department of EE-Systems, Computer Engineering Division, University of Southern California, 3740 McClintock Avenue, EEB 200C, 90089-2562, Los Angeles, CA, USA

    Viktor K. Prasanna

  3. Independent Consultant, c/o Infosys Ltd., “Mangala”, Kuloor Ferry Road, Kottara, 575006, Mangalore, India

    Sriram Vajapeyam

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© 2001 Springer-Verlag Berlin Heidelberg

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Ganguly, N., Das, A., Maji, P., Sikdar, B.K., Pal Chaudhuri, P. (2001). Evolving Cellular Automata Based Associative Memory for Pattern Recognition. In: Monien, B., Prasanna, V.K., Vajapeyam, S. (eds) High Performance Computing — HiPC 2001. HiPC 2001. Lecture Notes in Computer Science, vol 2228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45307-5_11

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  • DOI: https://doi.org/10.1007/3-540-45307-5_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43009-4

  • Online ISBN: 978-3-540-45307-9

  • eBook Packages: Springer Book Archive

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