Normalization methods for input and output vectors in backpropagation neural networks

Abstract

Neural networks have been increasingly applied to many problems in many areas, and Backpropagation has been the most popular neural network model. Despite its wide application, there are some major issues to be considered before using the model, such as the network topology, learning parameter, and normalization methods for the input and output vectors.

Input and output vectors for Backpropagation need to be normalized properly in order to achieve the best performance of the network. In this research, several normalization methods have been studied theoretically and two methods have been compared for performance in terms of prediction accuracy on the test sets through experiments with real world image data

Keywords:

• Neural networks
• backpropagation
• normalization
• pattern recognition