Cellular automata (CA) with given evolution rules have been widely investigated, but the inverse problem of extracting CA rules from observed data is less studied. Current CA rule extraction approaches are both time-consuming, and inefficient when selecting neighborhoods’. We give a novel approach to identifying CA rules from observed data, and selecting CA neighborhoods’ based on the identified CA model. Our identification algorithm uses a model linear in its parameters, and gives aunified framework for representing the identification problem for both deterministic and probabilistic cellular automata. Parameters are estimated based on a minimum-variance criterion.An incremental procedure is applied during CA identification to select an initial coarse neighborhood. Redundant cells in the neighborhood are then removed based on parameter estimates, and the neighborhood size is determined using a Bayesian information criterion. Experimental results show the effectiveness of our algorithm, and that it outperforms other leading CA identification algorithms.
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Cellular automata (CA), neighborhood selection,rule identification.