Fast rule identification and neighborhood selection for cellular automata

Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766, Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884 and Martin, Ralph Robert 2011. Fast rule identification and neighborhood selection for cellular automata. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41 (3) , pp. 749-760. 10.1109/TSMCB.2010.2091271

Full text not available from this repository.

Abstract

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 a unified framework for representing the identification problem for both deterministic and probabilistic CA. 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 the Bayesian information criterion. Experimental results show the effectiveness of our algorithm and that it outperforms other leading CA identification algorithms.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords:	Cellular automata (CA), neighborhood selection, rule identification
Publisher:	IEEE
ISSN:	1083-4419
Last Modified:	03 Oct 2024 14:12
URI:	https://orca.cardiff.ac.uk/id/eprint/11454

Citation Data

Cited 23 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item