State quantized sampled-data control design for complex-valued memristive neural networks

In this paper, several resultful control schemes based on data quantization are proposed for complex-valued memristive neural networks (CVMNNs). Firstly, considering the finite communication resources and the interference of failures to the system, a state quantized sampled-data controller (SQSDC) i...

Full description

Saved in:
Bibliographic Details
Published in:Journal of the Franklin Institute 2022-06, Vol.359 (9), p.4019-4053
Main Authors: Cai, Li, Xiong, Lianglin, Cao, Jinde, Zhang, Haiyang, Alsaadi, Fawaz E.
Format: Article
Language:English
Subjects:
Control systems design
Data analysis
Interference
Neural networks
Stability criteria
Validity
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, several resultful control schemes based on data quantization are proposed for complex-valued memristive neural networks (CVMNNs). Firstly, considering the finite communication resources and the interference of failures to the system, a state quantized sampled-data controller (SQSDC) is designed for CVMNNs. Next, taking the interference of gain fluctuations into account, a non-fragile sampled-data control (SDC) law is proposed for CVMNNs in the framework of data quantification. In order to full capture more inner sampling information, a newly Lyapunov-Krasovskii function (LKF) is constructed on the basis of the proposed triple integral inequality. After that, in the framework of taking full advantage of the property of Bessel-Legendre inequality, a time-dependent discontinuous LKF (TDDLKF) is proposed for CVMNNs with SQSDC. Based on the useful LKF, several stability criteria are established. Finally, the numerical simulations are provided to substantiate the validity and less conservatism of the proposed schemes.
ISSN:0016-0032
1879-2693
0016-0032
DOI:10.1016/j.jfranklin.2022.04.016