Template learning of cellular neural network using genetic programming

International Journal of Neural Systems
Elsayed Radwan, Eiichiro Tazaki

Abstract

A new learning algorithm for space invariant Uncoupled Cellular Neural Network is introduced. Learning is formulated as an optimization problem. Genetic Programming has been selected for creating new knowledge because they allow the system to find new rules both near to good ones and far from them, looking for unknown good control actions. According to the lattice Cellular Neural Network architecture, Genetic Programming will be used in deriving the Cloning Template. Exploration of any stable domain is possible by the current approach. Details of the algorithm are discussed and several application results are shown.

References

Aug 1, 1997·Neural Networks : the Official Journal of the International Neural Network Society·Michele Zamparelli
Mar 23, 2004·International Journal of Neural Systems·Elsayed Radwan, Eiichiro Tazaki
Jul 1, 1992·TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·T R Famula

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