- Tytuł:
- Robust zeroing neural networks with two novel power-versatile activation functions for solving dynamic Sylvester equation
- Autorzy:
-
Zhou, Peng
Tan, Mingtao - Powiązania:
- https://bibliotekanauki.pl/articles/2173674.pdf
- Data publikacji:
- 2022
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
recurrent neural network
RNN
zeroing neural network
ZNN
robust zeroing neural network
RZNN
fixed-time convergence
rekurencyjna sieć neuronowa
zerowanie sieci neuronowej
konwergencja w ustalonym czasie - Opis:
- In this work, two robust zeroing neural network (RZNN) models are presented for online fast solving of the dynamic Sylvester equation (DSE), by introducing two novel power-versatile activation functions (PVAF), respectively. Differing from most of the zeroing neural network (ZNN) models activated by recently reported activation functions (AF), both of the presented PVAF-based RZNN models can achieve predefined time convergence in noise and disturbance polluted environment. Compared with the exponential and finite-time convergent ZNN models, the most important improvement of the proposed RZNN models is their fixed-time convergence. Their effectiveness and stability are analyzed in theory and demonstrated through numerical and experimental examples.
- Źródło:
-
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e141307
0239-7528 - Pojawia się w:
- Bulletin of the Polish Academy of Sciences. Technical Sciences
- Dostawca treści:
- Biblioteka Nauki
Artykuł