Artificial neural networks: Challenges in science and engineering applications

Abstract

In this paper, artificial neural networks applications in the prediction field are described. The aim is to analyze the potentialities of conventional neural networks, such as feedforward neural networks, recurrent neural networks; and also, the potentialities of nonconventional neural networks composed typically by higher-order neural units. Finally, experimental analysis of long-term prediction of non-stationary time series (Mackey-Glass) is presented as well. The resulting prediction made by the proposed neural models feedforward multilayer perceptron and quadratic neural unit show high prediction accuracy for non-stationary time series.