PURPOSE: To reduce the error with movement of a separate plane and to learn a neural network at a high speed by using only a pattern of a large error at the first stage of learning.
CONSTITUTION: The input signal pattern of the teacher signal data 10 is inputted to the input layer neuron (S1). Then, the input signals are successively transmitted to the neurons of an output layer and the output of the output layer neuron is finally obtained (S2). An output error is calculated from the output signal of the data 10 and the calculated output of the output layer neuron (S3). Then, it is decided whether the learning pattern under display satisfies the weight correction conditions or not (S4). Then, the sharpest falling slope of an input pattern is calculated to the weight of each neuron (S5), and the correction value of this time is calculated from the error slope and the precedent correction value (S6). This operation is repeated until the output error is set less than the upper limit level of a fixed error that serves as a standard for the end of learning.
SAKO YUTAKA
ABE MASAHIRO
