期刊名称：Issues in Informing Science and Information Technology
印刷版ISSN：1547-5840
电子版ISSN：1547-5867
出版年度：2005
卷号：2
页码：787-787
出版社：Informing Science Institute
摘要：There are many successful applications of Backpropagation (BP) for training multilayer neural
networks. However, they have many shortcomings. Learning often takes insupportable time to
converge, and it may fall into local minima at all. One of the possible remedies to escape from
local minima is using a very small learning rate, but this will slow the learning process. The proposed
algorithm is presented for the training of multilayer neural networks with very small learning
rate, especially when using large training set size. It can apply in a generic manner for any
network size that uses a backpropgation algorithm through optical time. This paper studies the
performance of the Optical Backpropagation algorithm OBP (Otair & Salameh, 2004a, 2004b.
2005) on training a neural network for online handwritten character recognition in comparison
with backpropagation BP.
