Abstract
This paper reports a Cellular Automata Machine (CAM) as a general purpose pattern recognizer. The CAM is designed around a general class of CA known as Generalized Multiple Attractor Cellular Automata (GMACA). Experimental results confirm that the sparse network of CAM is more powerful than conventional dense network of Hopfield Net for memorizing unbiased patterns.
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© 2002 Springer-Verlag Berlin Heidelberg
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Maji, P., Ganguly, N., Saha, S., Roy, A.K., Chaudhuri, P.P. (2002). Cellular Automata Machine for Pattern Recognition. In: Bandini, S., Chopard, B., Tomassini, M. (eds) Cellular Automata. ACRI 2002. Lecture Notes in Computer Science, vol 2493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45830-1_26
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DOI: https://doi.org/10.1007/3-540-45830-1_26
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