Method for training artificial neural network using histograms and distributions to deactivate at least one hidden node

A computer program stored in a non-transitory computer readable storage medium, the computer program including a plurality of commands executed by one or more processors of a computing device, the computer program comprising:

• a command for inputting training data including normal data and abnormal data to an input layer of a neural network;
• a command for making a feature value output from each of one or more hidden nodes of a hidden layer of the neural network for each training data into a histogram and generating a distribution of the feature value for each of the one or more hidden nodes;
• a command for calculating an error between each distribution of the feature value and a predetermined probability distribution;
• and
• a command for updating a weight of at least one hidden node to deactivate the at least one hidden node of the one or more hidden nodes of the hidden layer based on the error.

A computing device for training at least a part of a neural network including a plurality of layers, the computing device comprising:

• one or more processors;
• and
• a memory, which stores commands executable by the one or more processors,
• wherein the one or more processors input training data including normal data and abnormal data to an input layer of the neural network, make a feature value output from each of one or more hidden nodes of a hidden layer of the neural network for each training data into a histogram and generate a distribution of the feature value for each of the one or more hidden nodes, calculate an error between each distribution of the feature value and a predetermined probability distribution, and update a weight of at least one hidden node to deactivate the at least one hidden node of the one or more hidden nodes of the hidden layer based on the error.