TY - JOUR
ID - 113906
TI - Chaotic Time Series Prediction Using Rough-Neural Networks
JO - Mathematics Interdisciplinary Research
JA - MIR
LA - en
SN - 2538-3639
AU - Ahmadi, Ghasem
AU - Dehghandar, Mohammad
AD - Department of Mathematics, Payame Noor University, Tehran, Iran
AD - Department of Mathematics, Payame Noor University, Tehran, Iran
Y1 - 2023
PY - 2023
VL - 8
IS - 2
SP - 71
EP - 92
KW - Artificial Neural Network
KW - Rough-neural network
KW - Time Series Prediction
KW - Lyapunov-based learning algorithm
KW - Lyapunov stability theory
DO - 10.22052/mir.2023.242878.1290
N2 - 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.
UR - https://mir.kashanu.ac.ir/article_113906.html
L1 - https://mir.kashanu.ac.ir/article_113906_1d677dea546c1881bb4032aec4ca0a6f.pdf
ER -