Problems Encountered during Implementation of the Backpropagation

Abstract

Backpropagation is one of the most popular neural networks and is widely applied in various problems. Despite its wide application, there are some major problems encountered during implementation of Backpropagation such as the appropriate decision for the network topology, learning parameter, initial weights, and data sets for training. Although the network performance is sensitive to these elements which should be determined prior to the implementation of Backpropagation, there is no efficient rule for the proper choice of these to achieve the best performance.

In this research, the effects of the learning rate, the learning mode, network topology, initial weights, and error goal for stopping the training have been studied through various experiments. Some significant results, which could be efficient for many applications of Backpropagation, were achieved in this research.

Keywords:

• Neural networks
• Ba[cgrave]
• kpropagation
• Learning rate
• Learning mode
• Network topology
• Initial weights