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Chouikhi, N., Ammar, B., Rokbani, N. and Alimi, A. (2017) PSO-Based Analysis of Echo State Network Parameters for Time Series Forecasting. Applied Soft Computing, 55, 211-225. https://doi.org/10.1016/j.asoc.2017.01.049

has been cited by the following article:

• TITLE: A Prediction Method Based on Improved Echo State Network for COVID-19 Nonlinear Time Series

  AUTHORS: Banteng Liu, Wei Chen, Yourong Chen, Ping Sun, Heli Jin, Hao Chen

  KEYWORDS: COVID-19, Nonlinear Time Series, Prediction, Echo State Network

  JOURNAL NAME: Journal of Computer and Communications, Vol.8 No.12, December 24, 2020

  ABSTRACT: This paper proposes a prediction method based on improved Echo State Network for COVID-19 nonlinear time series, which improves the Echo State Network from the reservoir topology and the output weight matrix, and adopt the ABC (Artificial Bee Colony) algorithm based on crossover and crowding strategy to optimize the parameters. Finally, the proposed method is simulated and the results show that it has stronger prediction ability for COVID-19 nonlinear time series.