A computational study on the performance of artificial neural networks under changing structural design and data distribution

This paper provides the results of our computational studies on artificial neural networks (ANNs) under various structural design and data distributions. A two-group classification problem is investigated. Simulated data with varying kurtosis and variance are used to examine how the ANN performs with respect to certain structural design (size and addition of input and weight noise) characteristics. The results of our study indicate that additive noise, size, and data distribution characteristics play an important role in learning, reliability and predictive ability of ANNs.