A Rule-Based Method to Locate the Bounds of Neural Networks

Abstract

An advanced method of training artificial neural networks is presented here which aims to identify the optimal interval for the initialization and training of artificial neural networks. The location of the optimal interval is performed using rules evolving from a genetic algorithm. The method has two phases: in the first phase, an attempt is made to locate the optimal interval, and in the second phase, the artificial neural network is initialized and trained in this interval using a method of global optimization, such as a genetic algorithm. The method has been tested on a range of categorization and function learning data and the experimental results are extremely encouraging.