Solving the density classification problem with a large diffusion and small amplification cellular automatonReport as inadecuate




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One of the most studied inverse problems in cellular automata CAs is the density classification problem.It consists in finding a CA such that, given any initial configuration of 0s and 1s, it converges to the all-1 fixed point configuration if the fraction of 1s is greater than the critical density 1-2, and it convergesto the all-0 fixed point configuration otherwise. In this paper, we propose an original approach to solvethis problem by designing a CA inspired by two mechanisms that are ubiquitous in nature: diffusion andnonlinear sigmoidal response. This CA, which is different from the classical ones because it has manystates, has a success ratio of 100%, and works for any system size, any dimension, and any critical density.Nota general

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Author: Briceño, Raimundo; - Rapaport Zimermann, Iván; - Espanés, Pablo Moisset de; - Osses Alvarado, Axel; -

Source: http://repositorio.uchile.cl/



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