A generalization of the Cellular Neural Network paradigm is obtained by removing the uniformity constraint on weight values. Such Generalized CNNs are capable of new tasks, such as function approximation or associative memory. A stability analysis of these networks is presented. Adaptation and application of a gradient descent learning algorithm is then discussed.
Generalized CNN: potentials of a CNN with non-uniform weights / Balsi, Marco. - STAMPA. - (1992), pp. 129-134. (Intervento presentato al convegno Second IEEE International Workshop on Cellular Networks and their Applications tenutosi a Monaco, Germania).
Generalized CNN: potentials of a CNN with non-uniform weights
BALSI, Marco
1992
Abstract
A generalization of the Cellular Neural Network paradigm is obtained by removing the uniformity constraint on weight values. Such Generalized CNNs are capable of new tasks, such as function approximation or associative memory. A stability analysis of these networks is presented. Adaptation and application of a gradient descent learning algorithm is then discussed.| File | Dimensione | Formato | |
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