scholarly journals Online Handwritten Character Recognition Using an Optical Backpropagation Neural Networks

10.28945/2932 ◽  
2005 ◽  
Author(s):  
Walid A. Salameh ◽  
Mohammed A. Otair
Keyword(s):  
Neural Networks ◽  
Character Recognition ◽  
Network Size ◽  
Learning Rate ◽  
Local Minima ◽  
Handwritten Character Recognition ◽  
Set Size ◽  
Handwritten Character ◽  
Multilayer Neural Networks ◽  
Optical Time

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.

scholarly journals Speeding Up Back-Propagation Neural Networks

10.28945/2931 ◽  
2005 ◽  
Author(s):  
Mohammed A. Otair ◽  
Walid A. Salameh
Keyword(s):  
Neural Networks ◽  
Back Propagation ◽  
Network Size ◽  
Learning Rate ◽  
Network Architectures ◽  
Local Minima ◽  
Set Size ◽  
Long Time ◽  
Multilayer Neural Networks ◽  
Optical Time

There are many successful applications of Backpropagation (BP) for training multilayer neural networks. However, it has many shortcomings. Learning often takes long time to converge, and it may fall into local minima. One of the possible remedies to escape from local minima is by using a very small learning rate, which slows down the learning process. The proposed algorithm presented in this study used for training depends on a multilayer neural network with a very small learning rate, especially when using a large training set size. It can be applied in a generic manner for any network size that uses a backpropgation algorithm through an optical time (seen time). The paper describes the proposed algorithm, and how it can improve the performance of back-propagation (BP). The feasibility of proposed algorithm is shown through out number of experiments on different network architectures.

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