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Identification and realization of linearly separable Boolean functions via cellular neural networks. (English) Zbl 1165.94331

Summary: In this paper, an effective method for identifying and realizing linearly separable Boolean functions (LSBF) of six variables via Cellular Neural Networks (CNN) is presented. We characterized the basic relations between CNN genes and the truth table of Boolean functions. In order to implement LSBF independently, a directed graph is employed to sort the offset levels according to the truth table.

MSC:

94C10 Switching theory, application of Boolean algebra; Boolean functions (MSC2010)
92B20 Neural networks for/in biological studies, artificial life and related topics
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