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Information Journal Paper

Title

Chaotic Time Series Prediction Using Rough-Neural Networks

Pages

  71-92

Abstract

 ‎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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