University of KashanMathematics Interdisciplinary Research2538-36398220230801Chaotic Time Series Prediction Using Rough-Neural Networks719211390610.22052/mir.2023.242878.1290ENGhasem AhmadiDepartment of Mathematics, Payame Noor University, Tehran, Iran0000-0003-1331-7253Mohammad DehghandarDepartment of Mathematics, Payame Noor University, Tehran, IranJournal Article20210604Artificial 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.https://mir.kashanu.ac.ir/article_113906_1d677dea546c1881bb4032aec4ca0a6f.pdf