Optimal Parameters of Bp Network for Character Recognition

The experiments showed that the structure of artificial neural networks and parameters strongly influenced the performance of the network [1]. If the network structure is too simple, it will lower the classification capacity of the network, thus affecting the final recognition result. If the network structure is too complex it will slower the learning speed and eventually the final error may increase. Also the momentum, learning rate parameters will also affect the speed of network learning and the final accuracy. If the most suitable network structure and network parameters are used it will get a best result [2]. In this paper, we will analysis the number of hidden units, learning rate on the impact of network learning speed and final accuracy, based on BP network in the character image recognition as an example.