Summary form only given. In the backpropagation process, the amendment of interconnecting weights given to the units of the input layer or hidden layer is calculated by the momentum ( alpha ) and the learning rate ( eta ). The number of training cycles, therefore, depends on alpha and eta , so that it is necessary to choose the most suitable values for alpha and eta . By changing alpha and eta , the authors tried to search for the most suitable values for the learning. The combinations alpha and eta behave under the constant rule, which is represented by eta =K(1- alpha ). Moreover, the constant K is determined by the ratio between the number of output units and hidden units. This conclusion is very important for deciding the size of a neural network.<<ETX>>