Chaotic Time Series Prediction Using Rough-Neural Networks
Author(s):
Article Type:
Research/Original Article (دارای رتبه معتبر)
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.
Keywords:
Language:
English
Published:
Mathematics Interdisciplinary Research, Volume:8 Issue: 2, Spring 2023
Pages:
71 to 92
https://www.magiran.com/p2616498
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