<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

In this paper, the problem of feedback stabilization for a class of impulsive state-dependent neural networks (ISDNNs) with nonlinear disturbance inputs via...

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

Online veröffentlicht: 04. Juli 2020

Seitenbereich: 267 - 279

Eingereicht: 06. Juni 2019

Akzeptiert: 25. Jan. 2020

DOI: https://doi.org/10.34768/amcs-2020-0021

Schlüsselwörter
state-dependent neural networks, quantized input, stabilization

© 2020 Yaxian Hong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Online veröffentlicht: 04. Juli 2020

Seitenbereich: 267 - 279

Eingereicht: 06. Juni 2019

Akzeptiert: 25. Jan. 2020

DOI: https://doi.org/10.34768/amcs-2020-0021

Schlüsselwörterstate-dependent neural networks, quantized input, stabilization

© 2020 Yaxian Hong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
state-dependent neural networks, quantized input, stabilization