Abstract
Classification of Boolean functions requires specific software or circuits to determine the class of a function or even to distinguish between two different classes. In order to provide a less costly solution, we study the approximation of the NPN function classification by a artificial neural network (ANN), and shown that there are configurations of ANN that can perfectly classify four-bit Boolean functions. Additionally, we look at the possibility of learning the classification of four-bit Boolean functions using a set of three-bit Boolean neural classifiers, and determine the scalability. Finally we also learn a discriminator that can distinguish between two functions and determine their similarity or difference in their NPN classes. As a result we show that the approximate neural function classification is a convenient approach to implement an efficient classifier and class discriminator directly from the data.
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Lukac, M., Podlaski, K., Kameyama, M. (2022). Approximate Function Classification. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13351. Springer, Cham. https://doi.org/10.1007/978-3-031-08754-7_28
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DOI: https://doi.org/10.1007/978-3-031-08754-7_28
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