APPARATUS FOR CLASSIFYING ABNORMAL DATA USING ARTIFICIAL NEURAL NETWORKS BASED ON FINE CHANGE DATA AND SPATIAL DATA

摘要

The present invention relates to an apparatus for classifying abnormal data using an artificial neural network and a method thereof. The method includes: a generation vector step of generating a generation vector by a generation module, which is a component of the abnormal data classification module, trained to generate a normal vector that is a multidimensional vector of normal time series distribution data based on a latent variable; a classification target vector step of receiving a classification target vector that is a multidimensional vector of classification target data that is a target for abnormal data classification; an abnormal data score output step of outputting an abnormal data score that is a loss value based on a difference between the generation vector and the classification target vector; and a potential variable adjustment step of adjusting a potential variable in a direction in which the abnormal data score becomes lower.;COPYRIGHT KIPO 2020