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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© 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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