| Autor: |
Ghasem Ahmadi, Mohammad Dehghandar |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Mathematics Interdisciplinary Research, Vol 8, Iss 2, Pp 71-92 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2476-4965 |
| DOI: |
10.22052/mir.2023.242878.1290 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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