CA and Cellular Automata: A Statistical Approach for Deterministic Machines

CA and Cellular Automata: A Statistical Approach for Deterministic Machines Laurent Deniau and Jacques Blanc-Talon
Abstract
	Any one-dimensional cellular automata is considered as a black box whose input/output (I/O) behaviour is analysed by using Principal Component Analysis (PCA). PCA computes the best subspace which maximises the projection of the I/O patterns onto the principal axis. Stability of the components is shown which leads to a linearisation of the CA regardless of the size of the patterns. The possibility of solving the inverse problem with a given accuracy is discussed.

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