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Identification of the neighborhood and CA rules from spatio-temporal CA patterns

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

Extracting the rules from spatio-temporal patterns generated by the evolution of cellular automata (CA) usually produces a CA rule table without providing a clear understanding of the structure of the neighborhood or the CA rule. In this paper, a new identification method based on using a modified orthogonal least squares or CA-OLS algorithm to detect the neighborhood structure and the underlying polynomial form of the CA rules is proposed. The Quine-McCluskey method is then applied to extract minimum Boolean expressions from the polynomials. Spatio-temporal patterns produced by the evolution of 1D, 2D, and higher dimensional binary CAs are used to illustrate the new algorithm, and simulation results show that the CA-OLS algorithm can quickly select both the correct neighborhood structure and the corresponding rule.

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:33 ,  Issue: 2 )