Classifying Three-input Boolean Functions by Neural Networks
Naoshi Sakamoto · 2019
Neural networks decide or predict something by learning examples. Recently, improving equips and enlarging data enable the deep learning to perform complex decision. However, some objects require a lot of learning time to improve the precision.In this study, we investigate the number of required epochs for neural networks to study three-input Boolean functions. Then, we find that the number of required epochs depends on each of Boolean functions. Thus, by focusing on the structure of Boolean functions, we find that we can estimate the minimum number of required epochs according to the property of disjunctive normal form of the Boolean function.