Proceedings of the 2004 11th IEEE International Conference on Electronics, Circuits and Systems, 2004. ICECS 2004.
DOI: 10.1109/icecs.2004.1399645
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A learnable self-feedback ratio-memory cellular nonlinear network (SRMCNN) for associative memory applications

Abstract: A self-feedback ratio-memory cellular nonlinear network (SRMCNN) with the B template and the modified Hebbian learning algorithm to learn and recognize the image patterns is proposed and analyzed. In the proposed SRMCNN, the coefficients of space-variant B templates are determined from the exemplar patterns during the learning period. The weights are the ratio of the absolute summation of its neighborhood weights in the B templates was stored in the associative memory. This SRMCNN can recognize the learned pat… Show more

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Cited by 4 publications
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References 18 publications
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