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Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks

Al-Jumeily, D, Ghazali, R and Hussain, A (2014) Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks. PLOS ONE, 9 (8). ISSN 1932-6203

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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 signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques.

Item Type: Article
Uncontrolled Keywords: MD Multidisciplinary
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: Public Library of Science
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Date Deposited: 20 Feb 2018 15:32
Last Modified: 04 Sep 2021 03:08
DOI or ID number: 10.1371/journal.pone.0105766
URI: https://researchonline.ljmu.ac.uk/id/eprint/8079
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