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Neighborhood detection using mutual information for the identification of cellular automata

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2 Author(s)
Zhao, Y. ; Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield, UK ; Billings, S.A.

Extracting the rules from spatio-temporal patterns generated by the evolution of cellular automata (CA) usually requires a priori information about the observed system, but in many applications little information will be known about the pattern. This paper introduces a new neighborhood detection algorithm which can determine the range of the neighborhood without any knowledge of the system by introducing a criterion based on mutual information (and an indication of over-estimation). A coarse-to-fine identification routine is then proposed to determine the CA rule from the observed pattern. Examples, including data from a real experiment, are employed to evaluate the new algorithm.

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:36 ,  Issue: 2 )