Error Signal Generation Method for Efficient Learning of Multi-layer Perceptron Neural Networks

摘要

In order to improve the performance of the back-propagation learning algorithm with M. perceptron, a new error function is proposed. The proposed error function generates an error signal as the target value of the output buffer becomes more different from the output value, thereby reducing the improper luxury of the output node during the neural network learning process. In addition, when the target value of the output layer approaches the output value, an error signal is generated small, thereby preventing the neural network from being excessively trained in the learning pattern.