Learning of cellular neural networks

摘要

In this paper an approach to first-order cellular neural networks (CNN) learning is suggested. It is based on the ideas of perceptron learning rule. The proposed method allows to find parameters of a CNN connection template which provides the formation of patterns with preset properties. The method was applied to learn ID and 2D CNN with symmetric templates and was verified by simulations. Experimental results are compared with the known ones theoretically obtained by Thiran et al. [P Thiran, K. Crounse, L.O. Chua, M. Hasler, IEEE Trans. Circuits and Systems-1 42 (10) (1995)] for CNN pattern properties.