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

Creator:

Hong, Yaxian ; Bin, Honghua ; Huang, Zhenkun

Date:

2020

Resource Type:

artykuł

Contributor:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Group publication title:

AMCS, volume 30 (2020)

Abstract:

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:86099

DOI:

10.34768/amcs-2020-0021

Pages:

267-279

Source:

AMCS, volume 30, number 2 (2020) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

Subject and keywords:

state-dependent neural networks quantized input stabilization

Object collections:

Last modified:

Jul 16, 2025

In our library since:

Jul 16, 2025

Number of object content hits:

9

All available object's versions:

https://zbc.uz.zgora.pl/repozytorium/publication/101151

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Stabilization analysis of impulsive state-dependent neural networks with nonlinear disturbance: A quantization approach Jul 16, 2025

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