A Delay-Based Reservoir Computing Model for Chaotic Series Prediction

Pavlidou, A., Liang, X. and Heidari, H. (2022) A Delay-Based Reservoir Computing Model for Chaotic Series Prediction. In: 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS 2022), Glasgow, UK, 24-26 October 2022, ISBN 9781665488235 (doi: 10.1109/ICECS202256217.2022.9971108)

Abstract

Conventional computers based on Von Neumann architecture are unable to process complex sequential data with high efficiency. This work investigates why Delay-based Reservoir Computing (DRC) is preferred this architecture, by performing Mackey-Glass chaotic time series prediction. The outcome of the prediction and the role of the Memory Capacity (MC) for such system are presented, with simulations done in MATLAB Simulink. This developed algorithm performs with a Mean Square Error (MSE) of 1.2314×10−3 per predicted digit.

• Item Type: Conference Proceedings
• Additional Information: This work was supported by the UK EPSRC under grant Industrial CASE (EP/W522168/1), Analog Neuromorphic Processing for Biosensors.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liang, Xiangpeng and Heidari, Professor Hadi and Pavlidou, Antonia
• Authors: Pavlidou, A., Liang, X., and Heidari, H.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• ISBN: 9781665488235

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