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

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 wit...

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Main Authors:	Hong Yaxian, Bin Honghua, Huang Zhenkun
Format:	Article
Language:	English
Published:	Sciendo 2020-06-01
Series:	International Journal of Applied Mathematics and Computer Science
Subjects:	state-dependent neural networks quantized input stabilization
Online Access:	https://doi.org/10.34768/amcs-2020-0021

id	doaj-46ef16f8206f4293af1652ce4cd4a12e
record_format	Article
spelling	doaj-46ef16f8206f4293af1652ce4cd4a12e2021-09-06T19:41:54ZengSciendoInternational Journal of Applied Mathematics and Computer Science2083-84922020-06-0130226727910.34768/amcs-2020-0021amcs-2020-0021Stabilization analysis of impulsive state–dependent neural networks with nonlinear disturbance: A quantization approachHong Yaxian0Bin Honghua1Huang Zhenkun2School of Science, Jimei University, Xiamen361021, Fujian Province, ChinaSchool of Science, Jimei University, Xiamen361021, Fujian Province, ChinaSchool of Science, Jimei University, Xiamen361021, Fujian Province, ChinaIn 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.https://doi.org/10.34768/amcs-2020-0021state-dependent neural networksquantized inputstabilization
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Hong Yaxian Bin Honghua Huang Zhenkun
spellingShingle	Hong Yaxian Bin Honghua Huang Zhenkun Stabilization analysis of impulsive state–dependent neural networks with nonlinear disturbance: A quantization approach International Journal of Applied Mathematics and Computer Science state-dependent neural networks quantized input stabilization
author_facet	Hong Yaxian Bin Honghua Huang Zhenkun
author_sort	Hong Yaxian
title	Stabilization analysis of impulsive state–dependent neural networks with nonlinear disturbance: A quantization approach
title_short	Stabilization analysis of impulsive state–dependent neural networks with nonlinear disturbance: A quantization approach
title_full	Stabilization analysis of impulsive state–dependent neural networks with nonlinear disturbance: A quantization approach
title_fullStr	Stabilization analysis of impulsive state–dependent neural networks with nonlinear disturbance: A quantization approach
title_full_unstemmed	Stabilization analysis of impulsive state–dependent neural networks with nonlinear disturbance: A quantization approach
title_sort	stabilization analysis of impulsive state–dependent neural networks with nonlinear disturbance: a quantization approach
publisher	Sciendo
series	International Journal of Applied Mathematics and Computer Science
issn	2083-8492
publishDate	2020-06-01
description	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.
topic	state-dependent neural networks quantized input stabilization
url	https://doi.org/10.34768/amcs-2020-0021
work_keys_str_mv	AT hongyaxian stabilizationanalysisofimpulsivestatedependentneuralnetworkswithnonlineardisturbanceaquantizationapproach AT binhonghua stabilizationanalysisofimpulsivestatedependentneuralnetworkswithnonlineardisturbanceaquantizationapproach AT huangzhenkun stabilizationanalysisofimpulsivestatedependentneuralnetworkswithnonlineardisturbanceaquantizationapproach
_version_	1717765116625158144

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

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