Asynchronous finite-time state estimation for semi-Markovian jump neural networks with randomly occurred sensor nonlinearities

被引：12

作者：

Wang, Yao ^{[1
]}

Xu, Shengyuan ^{[1
]}

Li, Yongmin ^{[2
]}

Chu, Yuming ^{[2
]}

Zhang, Zhengqiang ^{[3
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China

[2] Huzhou Univ, Sch Sci, Huzhou 313000, Zhejiang, Peoples R China

[3] Qufu Normal Univ, Sch Elect Engn & Automat, Rizhao 276826, Peoples R China

来源：

NEUROCOMPUTING | 2021年 / 432卷

关键词：

Asynchronous state estimation; Finite-time analysis; Semi-Markovian jump; Neural networks; Leakage delay; H-INFINITY CONTROL; SYNCHRONIZATION CONTROL; EXPONENTIAL STABILITY; SYSTEMS; DELAY; DESIGN;

D O I：

10.1016/j.neucom.2020.12.027

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper addresses the finite-time state estimation problem for semi-Markovian jump neural networks with sensor nonlinearities under the consideration of leakage delay and time-varying delay. The modes of original system and desired estimator are supposed to be asynchronous. Some sufficient conditions are proposed to guarantee the finite-time boundedness as well as mixed H-infinity and passive performance of the error system in terms of constructing Lyapunov-Krasovskii functionals. By utilizing affine Bessel-Legendre inequalities, a less conservative result can be achieved. By virtue of linear matrices inequalities approach, the asynchronous state estimator gains are obtained. Two numerical examples are provided to demonstrate the less conservativeness and effectiveness of our method. (C) 2020 Elsevier B.V. All rights reserved.

引用

页码：240 / 249

页数：10

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