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A very effective density classifier two-dimensional cellular automaton with memory

Ramón Alonso-Sanz1,2 and Larry Bull1

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Conventional cellular automata (CA) are memoryless, i.e. the new state of a cell depends on the neighborhood configuration solely at the preceding time step. This paper considers an extension to the standard framework of CA by implementing memory capability in cells. It is shown that the HPP rule, one of the most important block automaton rules, endowed with the memory of the most frequent recent state, behaves as an excellent classifier of the density in the initial configuration, which surpasses the performance of the best two-dimensional density classifier reported in the literature.


PACS

05.45.-a Nonlinear dynamics and nonlinear dynamical systems

05.50.+q Lattice theory and statistics (Ising, Potts, etc.)

MSC

37B15 Cellular automata

Subjects

Statistical physics and nonlinear systems

Dates

Issue 48 (4 December 2009)

Received 27 March 2009, in final form 14 September 2009

Published 11 November 2009



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