Learning and prediction method of irregular time series data using a recurrent neural network

摘要

PPROBLEM TO BE SOLVED: To provide a learning/forecasting method for irregular time serial data using a recurrent neural network capable of improving forecast accuracy of the irregular time series data than before. PSOLUTION: A recurrent neural network is used during learning. The monotone function is used as an activation function of a first neuron group N1 and 2r pieces of neurons 11-1r and 21-2R structuring a second neuron group N2 included in an input layer 1 of the recurrent neural network. The sinusoidal wave function is used as an activation function of q pieces of neurons 31-3q structuring a third neuron group N3 included in an intermediate layer 3. Further, the sinusoidal wave function is used as an activation function of r pieces of neurons 51-5R structuring a fourth neuron group N4 included in an output layer 5. PCOPYRIGHT: (C)2007,JPO&INPIT