Proceedings of the
The 33rd European Safety and Reliability Conference (ESREL 2023)
3 – 8 September 2023, Southampton, UK
On the Construction of Numerical Models through a Prime Convolutional Approach
1Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy.
2Mathema, Italy.
3Independent researcher.
ABSTRACT
In this paper we apply neural network models to a set of natural numbers in order to classify the congruence classes modulo a given integer m ∈ {2, 3, …, 10}. We compare the performances of two kinds of architectures and of several input data representations. It turns out that these tasks are fully solved using a convolutional architecture and a special representation for the input data that exploits the prime factor decomposition of numbers.
Keywords: Neural networks, Natural numbers, Convolutional networks, Prime numbers, Congruence classes.