Abstract: Disclosed is an apparatus for generating an artificial-neural-network-based prediction model. The apparatus includes an input data conversion unit configured to convert input data of an L-dimensional array (L is a natural number) into normalized vector data and input the normalized vector data, a modeling unit configured to model an artificial-neural-network-based prediction model for learning the input vector data and output a value predicted through the modeling, and an adjustment unit configured to compare the value predicted by the modeling unit with an actually measured value to calculate an error value and adjust learning parameters of an artificial neural network using the error value and a back-propagation algorithm.
Type:
Grant
Filed:
May 31, 2017
Date of Patent:
February 15, 2022
Assignees:
Seoul National University R&DB Foundation, eCubesolutions Co., Ltd.
Abstract: Disclosed is an apparatus for generating an artificial-neural-network-based prediction model. The apparatus includes an input data conversion unit configured to convert input data of an L-dimensional array (L is a natural number) into normalized vector data and input the normalized vector data, a modeling unit configured to model an artificial-neural-network-based prediction model for learning the input vector data and output a value predicted through the modeling, and an adjustment unit configured to compare the value predicted by the modeling unit with an actually measured value to calculate an error value and adjust learning parameters of an artificial neural network using the error value and a back-propagation algorithm.
Type:
Application
Filed:
May 31, 2017
Publication date:
December 7, 2017
Applicants:
Seoul National University R&DB Foundation, eCubesolutions Co., Ltd.