Predicting physical time series using dynamic ridge polynomial neural networks.

ABSTRACT: Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of

SUBMITTER: Al-Jumeily D 

PROVIDER: S-EPMC4144909 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature