A new method for the re-implementation of threshold logic functions with cellular neural networks.
A new strategy is presented for the implementation of threshold logic functions with binary-output Cellular Neural Networks (CNNs). The objective is to optimize the CNNs weights to develop a robust implementation. Hence, the concept of generative set is introduced as a convenient representation of any linearly separable Boolean function. Our analysis of threshold logic functions leads to a complete algorithm that automatically provides an optimized generative set. New weights are deduced and a more robust CNN template assuming the same function can thus be implemented. The strategy is illustrated by a detailed example.
Bénédic
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Laboratoire MIPS, Université de Haute Alsace, 4 rue des Frères Lumière Mulhouse, France. yohann.benedic@uha.fr
Wira
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Mercklé
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