H∞ state estimation for discrete memristive neural networks with signal quantization and probabilistic time delay

In this paper, the problem of $ H_{\infty } $ state estimation is discussed for a class of delayed discrete memristive neural networks with signal quantization. A random variable obeying the Bernoulli distribution is used to describe the probabilistic time delay. A switching function is introduced t...

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Autores principales:	Le Feng, Liang Zhao, Liqun Ban
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	memristive neural networks (mnns) probabilistic time delay (ptd) logarithmic quantization $ h_{\infty } $ state estimation Control engineering systems. Automatic machinery (General) TJ212-225 Systems engineering TA168
Acceso en línea:	https://doaj.org/article/174742da321944369f94f5a1489eb705
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spelling	oai:doaj.org-article:174742da321944369f94f5a1489eb7052021-11-11T14:23:43ZH∞ state estimation for discrete memristive neural networks with signal quantization and probabilistic time delay2164-258310.1080/21642583.2021.1997670https://doaj.org/article/174742da321944369f94f5a1489eb7052021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/21642583.2021.1997670https://doaj.org/toc/2164-2583In this paper, the problem of $ H_{\infty } $ state estimation is discussed for a class of delayed discrete memristive neural networks with signal quantization. A random variable obeying the Bernoulli distribution is used to describe the probabilistic time delay. A switching function is introduced to reflect the state dependence of memristive connection weight on neurons. Our aim is to design a state estimator to ensure that the specified disturbance attenuation level is guaranteed. By using Lyapunov stability theory and inequality scaling techniques, the specific explicit expression of gain parameter is given. Finally, a numerical example is given to verify the effectiveness of the proposed estimation method.Le FengLiang ZhaoLiqun BanTaylor & Francis Grouparticlememristive neural networks (mnns)probabilistic time delay (ptd)logarithmic quantization $ h_{\infty } $ state estimationControl engineering systems. Automatic machinery (General)TJ212-225Systems engineeringTA168ENSystems Science & Control Engineering, Vol 9, Iss 1, Pp 764-774 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	memristive neural networks (mnns) probabilistic time delay (ptd) logarithmic quantization $ h_{\infty } $ state estimation Control engineering systems. Automatic machinery (General) TJ212-225 Systems engineering TA168
spellingShingle	memristive neural networks (mnns) probabilistic time delay (ptd) logarithmic quantization $ h_{\infty } $ state estimation Control engineering systems. Automatic machinery (General) TJ212-225 Systems engineering TA168 Le Feng Liang Zhao Liqun Ban H∞ state estimation for discrete memristive neural networks with signal quantization and probabilistic time delay
description	In this paper, the problem of $ H_{\infty } $ state estimation is discussed for a class of delayed discrete memristive neural networks with signal quantization. A random variable obeying the Bernoulli distribution is used to describe the probabilistic time delay. A switching function is introduced to reflect the state dependence of memristive connection weight on neurons. Our aim is to design a state estimator to ensure that the specified disturbance attenuation level is guaranteed. By using Lyapunov stability theory and inequality scaling techniques, the specific explicit expression of gain parameter is given. Finally, a numerical example is given to verify the effectiveness of the proposed estimation method.
format	article
author	Le Feng Liang Zhao Liqun Ban
author_facet	Le Feng Liang Zhao Liqun Ban
author_sort	Le Feng
title	H∞ state estimation for discrete memristive neural networks with signal quantization and probabilistic time delay
title_short	H∞ state estimation for discrete memristive neural networks with signal quantization and probabilistic time delay
title_full	H∞ state estimation for discrete memristive neural networks with signal quantization and probabilistic time delay
title_fullStr	H∞ state estimation for discrete memristive neural networks with signal quantization and probabilistic time delay
title_full_unstemmed	H∞ state estimation for discrete memristive neural networks with signal quantization and probabilistic time delay
title_sort	h∞ state estimation for discrete memristive neural networks with signal quantization and probabilistic time delay
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/174742da321944369f94f5a1489eb705
work_keys_str_mv	AT lefeng hstateestimationfordiscretememristiveneuralnetworkswithsignalquantizationandprobabilistictimedelay AT liangzhao hstateestimationfordiscretememristiveneuralnetworkswithsignalquantizationandprobabilistictimedelay AT liqunban hstateestimationfordiscretememristiveneuralnetworkswithsignalquantizationandprobabilistictimedelay
_version_	1718438914898788352

H∞ state estimation for discrete memristive neural networks with signal quantization and probabilistic time delay

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