%0 Journal Article
%T Chaotic Time Series Prediction Using Rough-Neural Networks
%J Mathematics Interdisciplinary Research
%I University of Kashan
%Z 2538-3639
%A Ahmadi, Ghasem
%A Dehghandar, Mohammad
%D 2023
%\ 08/01/2023
%V 8
%N 2
%P 71-92
%! Chaotic Time Series Prediction Using Rough-Neural Networks
%K Artificial Neural Network
%K Rough-neural network
%K Time Series Prediction
%K Lyapunov-based learning algorithm
%K Lyapunov stability theory
%R 10.22052/mir.2023.242878.1290
%X 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.
%U https://mir.kashanu.ac.ir/article_113906_1d677dea546c1881bb4032aec4ca0a6f.pdf