Observer-based Ho, control of memristor-based neural networks with unbounded time-varying delays

被引：3
|
作者：
Meng, Xianhe [1 ]
Wang, Yantao [1 ,2 ]
Liu, Chunyan [3 ]
机构：
[1] Heilongjiang Univ, Sch Math Sci, Harbin 150080, Peoples R China
[2] Heilongjiang Univ, Heilongjiang Prov Key Lab Theory & Computat Comple, Harbin 150080, Peoples R China
[3] Heilongjiang Univ, Sch Informat Management, Harbin 150080, Peoples R China
来源：
NEUROCOMPUTING | 2023年 / 549卷
基金：
中国国家自然科学基金;
关键词：
Memristor-based neural networks; Unbounded time-varying delays; Observer-based H o; control; Observer; Controller; EXPONENTIAL STABILITY; INFINITY CONTROL; STATE ESTIMATION; DESIGN; DISCRETE; SYSTEMS; INPUT;
D O I：
10.1016/j.neucom.2023.126357
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This work is devoted to developing observer-based Ho, control of memristor-based neural networks with unbounded time-varying delays. A suitable observer is first designed, and then the controller is implemented based on the estimated states. Taking into account the dynamic equation of the MNN and that of the observer error, an augmented closed-loop system is given. By proposing a system solutionsbased estimation method, sufficient conditions are obtained to guarantee that the augmented system is globally exponentially stable and satisfies a prescribed Ho, performance level. This approach requires neither model transformation nor the construction of Lyapunov-Krasovskii functionals. In addition, the obtained sufficient conditions contain only a few scalar inequalities, which can be easily addressed by MATLAB. Finally, illustrative simulations are given to test the validity of the theoretical results.& COPY; 2023 Elsevier B.V. All rights reserved.
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页数：13
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