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.
Cite
CITATION STYLE
Ganguly, N., Maji, P., Das, A., Sikdar, B. K., & Pal Chaudhuri, P. (2002). Characterization of non-linear cellular automata model for pattern recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2275, pp. 214–220). Springer Verlag. https://doi.org/10.1007/3-540-45631-7_29
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