LEARNING METHOD FOR NEURAL NETWORK

摘要

PROBLEM TO BE SOLVED: To learn a neural network with a high precision by obtaining a coupling coefficient by the back propagation method in accordance with the square sum between a neural network output error and the error of the differential value of a neural network output. SOLUTION: Normalized learning data is inputted to an N.N operation means 1 to calculate an N.N output. A differential value calculation means 2 first calculates a linear approximation formula obtained by total differentiation of a non-linear function with an input parameter. A difference valueΔX is inputted to this linear approximation formula to obtain a differential valueΔY of the N.N output. Meanwhile, a coupling coefficient change means 3 obtains the square error sum of an error (e) between an output Y of the N.N operation means 1 and a teacher signal (d) and an errorΔe between the differential valueΔY obtained by the differential value calculation means 2 and a difference valueΔd of the teacher signal. The square error sum is taken as an error function to update the coupling coefficient of the N.N operation means 1 by the back propagation method.