An architecture for encoding two - dimensional cyclone track prediction problem in coevolutionary recurrent neural networks

Chandra, R. and Deo, Ratneel and Omlin, C.W. (2016) An architecture for encoding two - dimensional cyclone track prediction problem in coevolutionary recurrent neural networks. [Conference Proceedings]

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Abstract

Cyclone track prediction is a two dimensional time series prediction problem that involves latitudes and longitudes which define the position of a cyclone. Recurrent neural networks have been suitable for time series prediction due to their architectural properties in modeling temporal sequences. Coevolutionary recurrent neural networks have been used for time series prediction and also applied to cyclone track prediction. In this paper, we present an architecture for encoding two dimensional time series problem into Elman recurrent neural networks composed of a single input neuron. We use cooperative coevolution and back-propagation through-time algorithms for training. Our experiments show an improvement in the accuracy when compared to previous results using a different recurrent network architecture.

Item Type:	Conference Proceedings
Subjects:	Q Science > Q Science (General)
Divisions:	Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
Depositing User:	Ratneel Deo
Date Deposited:	05 Jan 2017 02:51
Last Modified:	08 Jul 2019 22:59
URI:	http://repository.usp.ac.fj/id/eprint/9547

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