Title
<italic>H</italic><sub>∞</sub> State Estimation for Switched Inertial Neural Networks With Time-Varying Delays: A Persistent Dwell-Time Scheme
Abstract
The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_{\infty }$ </tex-math></inline-formula> state estimation issue for switched delayed inertial neural networks is addressed in this work. A more universal switching law, persistent dwell-time (DT) switching law, is considered here rather than average DT one of which switching frequency among subsystems is strictly limited, or DT one. Concurrently, time delays are inevitable when transmitting information, then taking the time-varying delays into account makes the constructed systems conform well with the actual situations. The main goal in the work is devoted to designing a state estimator to ensure that the state estimation error system is globally uniformly exponentially stable and satisfies a prescribed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_{\infty }$ </tex-math></inline-formula> noise attenuation level. A mixed time/mode-dependent Lyapunov–Krasovskii functional matched with the foregoing switching law is introduced. Through utilizing some reasonable inequalities and common matrix operations, some sufficient criteria which guarantee the aforesaid stability and the solvability of the addressed issue are presented. Finally, an illustrative example is provided to present the potentiality and validity of the developed results.
Year
DOI
Venue
2022
10.1109/TSMC.2021.3061768
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Keywords
DocType
Volume
Global uniform exponential stability,inertial neural networks (INNs),persistent dwell-time (DT) switching law,state estimation,time-varying delays (TVDs)
Journal
52
Issue
ISSN
Citations 
5
2168-2216
0
PageRank 
References 
Authors
0.34
28
5
Name
Order
Citations
PageRank
Jing Wang131.40
Xiaohui Hu2178.10
Jinde Cao311399733.03
Ju H. Park45878330.37
Hao Shen522432.93