Sampled-data state-estimation of delayed complex-valued neural networks

Nallappan Gunasekaran; Guisheng Zhai

doi:10.1080/00207721.2019.1704095

Sampled-data state-estimation of delayed complex-valued neural networks

Nallappan Gunasekaran, Guisheng Zhai

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

Abstract

This paper studies the sampled-data state-estimation problem of delayed complex-valued neural networks (CVNNs). By using Lyapunov–Krasovskii functional (LKF), standard integral inequality together with the reciprocal convex approach, a delay-dependent condition is established in terms of the solution to linear matrix inequalities (LMIs) such that the consider CVNNs is asymptotically stable. As a result, an estimator gain matrix can be obtained through sampling instant. Finally, a simulation example is given to illustrate the theoretical analysis.

Original language	English
Journal	International Journal of Systems Science
DOIs	https://doi.org/10.1080/00207721.2019.1704095
Publication status	Accepted/In press - 2019 Jan 1

Keywords

Complex-valued neural networks
integral inequality
linear matrix inequality
Lyapunov method
sampled-data control
state-estimation

ASJC Scopus subject areas

Control and Systems Engineering
Theoretical Computer Science
Computer Science Applications

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title = "Sampled-data state-estimation of delayed complex-valued neural networks",

abstract = "This paper studies the sampled-data state-estimation problem of delayed complex-valued neural networks (CVNNs). By using Lyapunov–Krasovskii functional (LKF), standard integral inequality together with the reciprocal convex approach, a delay-dependent condition is established in terms of the solution to linear matrix inequalities (LMIs) such that the consider CVNNs is asymptotically stable. As a result, an estimator gain matrix can be obtained through sampling instant. Finally, a simulation example is given to illustrate the theoretical analysis.",

keywords = "Complex-valued neural networks, integral inequality, linear matrix inequality, Lyapunov method, sampled-data control, state-estimation",

author = "Nallappan Gunasekaran and Guisheng Zhai",

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AU - Zhai, Guisheng

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N2 - This paper studies the sampled-data state-estimation problem of delayed complex-valued neural networks (CVNNs). By using Lyapunov–Krasovskii functional (LKF), standard integral inequality together with the reciprocal convex approach, a delay-dependent condition is established in terms of the solution to linear matrix inequalities (LMIs) such that the consider CVNNs is asymptotically stable. As a result, an estimator gain matrix can be obtained through sampling instant. Finally, a simulation example is given to illustrate the theoretical analysis.

AB - This paper studies the sampled-data state-estimation problem of delayed complex-valued neural networks (CVNNs). By using Lyapunov–Krasovskii functional (LKF), standard integral inequality together with the reciprocal convex approach, a delay-dependent condition is established in terms of the solution to linear matrix inequalities (LMIs) such that the consider CVNNs is asymptotically stable. As a result, an estimator gain matrix can be obtained through sampling instant. Finally, a simulation example is given to illustrate the theoretical analysis.

KW - Complex-valued neural networks

KW - integral inequality

KW - linear matrix inequality

KW - Lyapunov method

KW - sampled-data control

KW - state-estimation

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SN - 0020-7721

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Sampled-data state-estimation of delayed complex-valued neural networks

Abstract

Keywords

ASJC Scopus subject areas

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