Forecasting Natural Events Using Axonal Delay

Reid, David and Hussain, Abir Jaafar and Tawfik, Hissam (2019) Forecasting Natural Events Using Axonal Delay. In: IEEE Congress on Evolutionary Computation, July 2018, Rio de Janeiro.

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Abstract

The ability to forecast natural phenomena relies on understanding causality. By definition this understanding must include a temporal component. In this paper, we consider the ability of an emerging class of neural network, which encode temporal information into the network, to perform the difficult task of Natural Event Forecasting. The Axonal Delay Network (ADN) models axonal delay in order to make predictions about sunspot activity, the Auroral Electrojet (AE) index and daily temperatures during a heatwave. The performance of this network is benchmarked against older types of neural networks; including the Multi-Layer Perceptron (MLP) network and Functional Link Neural Network (FLNN). The results indicate that the inherent temporal characteristics of the Axonal Delay Network make it well suited to the processing and prediction of natural phenomena.

Item Type:	Conference or Workshop Item (Paper)
Additional Information and Comments:	This is the author's version of a paper that was presented at the IEEE Congress on Evolutionary Computation. The subsequent publication from the Congress is available from https://ieeexplore.ieee.org/document/8477831
Faculty / Department:	Faculty of Human and Digital Sciences > Mathematics and Computer Science
Depositing User:	David REID
Date Deposited:	23 May 2019 11:02
Last Modified:	23 May 2019 11:02
URI:	https://hira.hope.ac.uk/id/eprint/2854

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