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2015 | OriginalPaper | Chapter

Forecasting Weather Signals Using a Polychronous Spiking Neural Network

Authors : David Reid, Hissam Tawfik, Abir Jaafar Hussain, Haya Al-Askar

Published in: Intelligent Computing Theories and Methodologies

Publisher: Springer International Publishing

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Abstract

Due to its inherently complex and chaotic nature predicting various weather phenomena over non trivial periods of time is extremely difficult. In this paper, we consider the ability of an emerging class of temporally encoded neural network to address the challenge of weather forecasting. The Polychronous Spiking Neural Network (PSNN) uses axonal delay to encode temporal information into the network in order to make predictions about weather signals. The performance of this network is benchmarked against the Multi-Layer Perceptron network as well as Linear Predictor. The results indicate that the inherent characteristics of the Polychronous Spiking Network make it well suited to the processing and prediction of complex weather signals.

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previous chapter An Online Supervised Learning Algorithm Based on Nonlinear Spike Train Kernels
next chapter A Water Wave Optimization Algorithm with Variable Population Size and Comprehensive Learning
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Metadata
Title
Forecasting Weather Signals Using a Polychronous Spiking Neural Network
Authors
David Reid
Hissam Tawfik
Abir Jaafar Hussain
Haya Al-Askar
Copyright Year
2015
Book
Intelligent Computing Theories and Methodologies

Print ISBN: 978-3-319-22179-3

Electronic ISBN: 978-3-319-22180-9

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-22180-9_12

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