Stabilization analysis of impulsive state-dependent neural networks with nonlinear disturbance: A quantization approach

• Autorzy: Hong Yaxian , Bin Honghua , Huang Zhenkun

Identyfikatory

DOI

10.34768/amcs-2020-0021

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

state dependent neural network; quantized input; stabilization analysis

PL

sieć neuronowa; dane wejściowe kwantyzowane; analiza stateczności

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2020
• Tom: Vol. 30, no. 2
• Strony: 267--279
• Opis fizyczny: Bibliogr. 26 poz., wykr.

Twórcy

autor

Hong Yaxian

• School of Science, Jimei University, Xiamen 361021, Fujian Province, China

autor

Bin Honghua

• School of Science, Jimei University, Xiamen 361021, Fujian Province, China

autor

Huang Zhenkun

• School of Science, Jimei University, Xiamen 361021, Fujian Province, China

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).