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Forecasting Weather Signals Using a Polychronous Spiking Neural Network

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

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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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Author information

Authors and Affiliations

  1. Mathematics and Computer Science Department, Liverpool Hope University, Liverpool, UK

    David Reid & Hissam Tawfik

  2. Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK

    Abir Jaafar Hussain

  3. Department of Computer Science, Salman Bin Abdulaziz University, Al-Kharj, PO Box 151, 11942, KSA

    Haya Al-Askar

Authors
  1. David Reid
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  2. Hissam Tawfik
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  3. Abir Jaafar Hussain
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  4. Haya Al-Askar
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Corresponding author

Correspondence to Hissam Tawfik .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

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Reid, D., Tawfik, H., Hussain, A.J., Al-Askar, H. (2015). Forecasting Weather Signals Using a Polychronous Spiking Neural Network. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_12

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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