Template learning of cellular neural network using genetic programming.
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.
Radwan
Elsayed
E
Department of Control and System Engineering, Toin University of Yokohama, 1614 Kurogane-cho, Aoba-ku, Yokohama 225-8502, Japan. radwan@intlab.toin.ac.jp
Tazaki
Eiichiro
E
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D006801:Humans; D007858:Learning / Q000502:physiology*; D008957:Models, Genetic; D008959:Models, Neurological; D016571:Neural Networks, Computer; D013698:Templates, Genetic