Advancing nonlinear dynamics identification with recurrent quantum neural networks: Emphasizing Lyapunov stability and adaptive learning in system analysis
Identification of nonlinear dynamic systems is a critical task in various fields. Artificial neural networks have been widely used for this purpose due to their ability to approximate complex functions. However, their computational efficiency and stability often pose challenges, especially in real-t...
| Автори: | , , , , |
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| Формат: | Стаття |
| Мова: | English |
| Опубліковано: |
Elsevier
2024-12-01
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| Серія: | Alexandria Engineering Journal |
| Предмети: | |
| Онлайн доступ: | http://www.sciencedirect.com/science/article/pii/S1110016824010901 |