An Approximate Algorithm for Determining the Sign Function of a Number Using Neural Network Methods

Abstract

Determining the sign of a number is not as simple as addition and multiplication. When using the traditional notation of a number in binary form with two's complement code, it allows you to store the sign of the number and process it. However, when representing a number in modular form, or in any other forms, for example, in the form of a homomorphic cipher, where the operation of determining the sign cannot be performed explicitly. In such cases, it is necessary to resort to various computationally complex methods. In this work, we are conducting research on the possibility of using neural networks to calculate an approximate function of the sign of a number, this will reduce the computational costs of traditional approaches to determining the sign.