Dynamic learning algorithm of multi-layer perceptrons for letter recognition

The classical back-propagation learning algorithms of neural networks suffer from a major disadvantage that of excessive computational burden encountered by processing all the data. Relatively speaking, the samples near the separating boundary have a more important influent on the final weights than those far. This paper presents a dynamic back-propagation algorithm which is just based on those decision boundary samples. The dynamic back-propagation algorithm using those boundary samples to update weights can not only greatly improve the learning speed, but also can improve the classification correction. The experimental results for the Letter data set verified that the proposed method is effective. It is far faster than classical learning algorithm and gets 91.1% classification correction.