Abstract
In this paper we consider fast-convergence learning algorithms for multi-valued and universal binary neurons. These neurons are suggested to be used for the design of neural networks based on Cellular Neural Networks (CNN) —in the sense of connections between neurons. On the basis of such networks we offer a solution to some problems of image processing. For instance, a highly efficient method for contours distinguishing, obtained by the learning algorithm described in this paper is presented.
This work is supported by Maltian company DKL Ltd -198 Old Bakery Street, Valetta, Malta. Fax (356) 221893.
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© 1993 Springer-Verlag Berlin Heidelberg
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Aizenberg, N.N., Aizenberg, I.N. (1993). Fast-convergence learning algorithms for multi-level and binary neurons and solution of some image processing problems. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_152
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DOI: https://doi.org/10.1007/3-540-56798-4_152
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