New Fixed-Time Stability Lemmas and Applications to the Discontinuous Fuzzy Inertial Neural Networks

This article aims to analyze the fixed-time synchronization of a class of discontinuous fuzzy inertial neural networks with time-varying delays based on the new improved fixed-time stability lemmas. First of all, by using the generalized variable transformation and Filippov solution theory, the disc...

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Bibliographic Details
Published inIEEE transactions on fuzzy systems Vol. 29; no. 12; pp. 3711 - 3722
Main Authors Kong, Fanchao, Zhu, Quanxin, Huang, Tingwen
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Discontinuity
Filippov solution theory
fixed-time synchronization
fuzzy inertial neural networks
Fuzzy logic
Liapunov functions
Lyapunov functional
Lyapunov methods
Neural networks
Stability
Stability criteria
Synchronization
Time synchronization
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Summary:This article aims to analyze the fixed-time synchronization of a class of discontinuous fuzzy inertial neural networks with time-varying delays based on the new improved fixed-time stability lemmas. First of all, by using the generalized variable transformation and Filippov solution theory, the discontinuities of the considered neural system can be coped with, and the error system is established. By relaxing the conditions of the <inline-formula><tex-math notation="LaTeX">C</tex-math></inline-formula>-regular Lyapunov function, two new fixed-time stability lemmas are proved via simple inequality techniques. The setting times are also estimated and are more accurate in comparison with the previous ones. Finally, one typical numerical example is carried out to verify the correctness and the advantages of the main results.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2020.3026030