DYNAMIC NEURAL NETWORK TO HAVE LEARNING MECHANISM

摘要

PURPOSE:To normalize a presenting pattern to be a fixed continuing time length at a learning time, in which a coupling coefficient between units is determined, to present the pattern to a network and to cause an error in an output layer to be minimum by determining the coupling coefficient so that different between a teacher signal in the output layer and a real output value can be caused to be small. CONSTITUTION:A correcting quantity computing part 40 calculates the correcting quantity of the coupling coefficient with using learning data, which are sent from a time base matching part 30, and the coupling coefficient to be stored in an interunit coupling coefficient storing part 50 and sends the quantity to a coupling coefficient correcting part 60. The coupling coefficient correcting part 60 adds the correcting quantity to the coupling coefficient, which is stored in the interunit coupling coefficient storing part 50, and reloads the coefficient. The correcting quantity computing part 40 repeats this correcting operation until the correcting quantity to all the coefficients goes to be smaller than a threshold value, which is determined in advance, or until a correcting circuit goes over a number to be determined in advance. Thus, the fluctuation of a uttering time length in unknown sound data is normalized by a dynamic programming at the time of recognizing operation and inputted to a neural network.