A dynamic architecture for artificial neural networks

Abstract

Artificial neural networks (ANN), have shown to be an effective, general-purpose approach for pattern recognition, classification, clustering, and prediction. Traditional research in this area uses a network with a sequential iterative learning process based on the feed-forward, back-propagation algorithm. In this paper, we introduce a model that uses a different architecture compared to the traditional neural network, to capture and forecast nonlinear processes. This approach utilizes the entire observed data set simultaneously and collectively to estimate the parameters of the model. To assess the effectiveness of this method, we have applied it to a marketing data set and a standard benchmark from ANN literature (Wolf's sunspot activity data set). The results show that this approach performs well when compared with traditional models and established research.