Mathematics Interdisciplinary Research (Aug 2023)

Chaotic Time Series Prediction Using Rough-Neural Networks

  • Ghasem Ahmadi,
  • Mohammad Dehghandar

DOI
https://doi.org/10.22052/mir.2023.242878.1290
Journal volume & issue
Vol. 8, no. 2
pp. 71 – 92

Abstract

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‎Artificial neural networks with amazing properties‎, ‎such as universal approximation‎, ‎have been utilized to approximate the nonlinear processes in many fields of applied sciences‎. ‎This work proposes the rough-neural networks (R-NNs) for the one-step ahead prediction of chaotic time series‎. ‎We adjust the parameters of R-NNs using a continuous-time Lyapunov-based training algorithm‎, ‎and prove its stability using the continuous form of Lyapunov stability theory‎. ‎Then‎, ‎we utilize the R-NNs to predict the well-known Mackey-Glass time series‎, ‎and Henon map‎, ‎and compare the simulation results with some well-known neural models‎.

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