- Tytuł:
- Stabilization analysis of impulsive state-dependent neural networks with nonlinear disturbance: A quantization approach
- Autorzy:
-
Hong, Yaxian
Bin, Honghua
Huang, Zhenkun - Powiązania:
- https://bibliotekanauki.pl/articles/330511.pdf
- Data publikacji:
- 2020
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
state dependent neural network
quantized input
stabilization analysis
sieć neuronowa
dane wejściowe kwantyzowane
analiza stateczności - Opis:
- In this paper, the problem of feedback stabilization for a class of impulsive state-dependent neural networks (ISDNNs) with nonlinear disturbance inputs via quantized input signals is discussed. By constructing quasi-invariant sets and attracting sets for ISDNNs, we design a quantized controller with adjustable parameters. In combination with a suitable ISS-Lyapunov functional and a hybrid quantized control strategy, we propose novel criteria on input-to-state stability and global asymptotical stability for ISDNNs. Our results complement the existing ones. Numerical simulations are reported to substantiate the theoretical results and effectiveness of the proposed strategy.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2020, 30, 2; 267-279
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł