Intermittent State Observer Design for Neural Networks With Reaction-Diffusion Terms Using Partial Measurements

Xiaona Song; Mi Wang; Shuai Song; Choon Ki Ahn

doi:10.1109/TSMC.2023.3262936

Intermittent State Observer Design for Neural Networks With Reaction-Diffusion Terms Using Partial Measurements

Xiaona Song, Mi Wang, Shuai Song, Choon Ki Ahn

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

4 Citations (Scopus)

Abstract

This article develops a novel state observer for delayed reaction-diffusion neural networks by utilizing incomplete measurements. To reduce the transmission cost efficiently, the space domain is divided into $L$ parts and only partial information needs to be measured in every subdomain, such as a point in one-dimensional space, a line and a plane in two- and three-dimensional space, respectively. In addition, the time domain is divided: the measured output signals are transmitted intermittently. Then, new conditions that assure the asymptotic stability of observation error system are derived based on the Lyapunov direct method and several inequality techniques. Finally, the proposed approach's effectiveness is demonstrated via three numerical examples.

Original language	English
Pages (from-to)	5224-5235
Number of pages	12
Journal	IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume	53
Issue number	8
DOIs	https://doi.org/10.1109/TSMC.2023.3262936
Publication status	Published - 2023 Aug 1

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 62203153 and Grant 61976081; in part by the Natural Science Fund for Excellent Young Scholars of Henan Province under Grant 202300410127; in part by the Key Scientific Research Projects of Higher Education Institutions in Henan Province under Grant 22A413001; in part by the Top Young Talents in Central Plains under Grant Yuzutong (2021) 44; in part by the Technology Innovative Teams in University of Henan Province under Grant 23IRTSTHN012; in part by the Natural Science Fund for Young Scholars of Henan Province under Grant 222300420151; and in part by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (Ministry of Science and ICT) under Grant NRF-2020R1A2C1005449.

Publisher Copyright:
© 2022 IEEE.

Keywords

Incomplete measurements
intermittent
neural networks
reaction-diffusion
state estimation

ASJC Scopus subject areas

Software
Control and Systems Engineering
Human-Computer Interaction
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TSMC.2023.3262936

Cite this

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abstract = "This article develops a novel state observer for delayed reaction-diffusion neural networks by utilizing incomplete measurements. To reduce the transmission cost efficiently, the space domain is divided into $L$ parts and only partial information needs to be measured in every subdomain, such as a point in one-dimensional space, a line and a plane in two- and three-dimensional space, respectively. In addition, the time domain is divided: the measured output signals are transmitted intermittently. Then, new conditions that assure the asymptotic stability of observation error system are derived based on the Lyapunov direct method and several inequality techniques. Finally, the proposed approach's effectiveness is demonstrated via three numerical examples.",

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note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 62203153 and Grant 61976081; in part by the Natural Science Fund for Excellent Young Scholars of Henan Province under Grant 202300410127; in part by the Key Scientific Research Projects of Higher Education Institutions in Henan Province under Grant 22A413001; in part by the Top Young Talents in Central Plains under Grant Yuzutong (2021) 44; in part by the Technology Innovative Teams in University of Henan Province under Grant 23IRTSTHN012; in part by the Natural Science Fund for Young Scholars of Henan Province under Grant 222300420151; and in part by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (Ministry of Science and ICT) under Grant NRF-2020R1A2C1005449. Publisher Copyright: {\textcopyright} 2022 IEEE.",

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Intermittent State Observer Design for Neural Networks With Reaction-Diffusion Terms Using Partial Measurements

Abstract

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Keywords

ASJC Scopus subject areas

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