@misc{Hong_Yaxian_Stabilization,
 author={Hong, Yaxian and Bin, Honghua and Huang, Zhenkun},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 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.},
 type={artykuł},
 title={Stabilization analysis of impulsive state-dependent neural networks with nonlinear disturbance: A quantization approach},
 keywords={state-dependent neural networks, quantized input, stabilization},
}