Further Analysis on Fixed/Preassigned-Time Projective Synchronization of Discontinuous Fuzzy Delayed Inertial Neural Networks

Our goal is addressing the problems of preassigned-time projective synchronization and fixed-time projective synchronization of the system that studied in our article, and fuzzy terms, mixed delays, inertial terms and discontinuous activation function are also included in this system. Further, the results of this article are based on the latest fixed-time stabilization lemma. Through the construction of adaptative Lyapunov function and new corresponding feedback controllers, we arrive at the results of fixed-time projective synchronization and preassigned-time projective synchronization about investigated neural networks, and we are using a non-reduced-order approach. The proposed results are more extensive in comparison with traditional anti-synchronization and synchronization. At the end of our article, to demonstrate the advantage of our outcomes, we display some simulation example.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3582344
Citations
2
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
2.97

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article

Further Analysis on Fixed/Preassigned-Time Projective Synchronization of Discontinuous Fuzzy Delayed Inertial Neural Networks

李岩 Li Yan, Guodong Zhang, Xiao Zhou, Jing Han
2 citations
IEEE Access
Neural Networks Stability and Synchronization
2.97
article

Further Analysis on Fixed/Preassigned-Time Projective Synchronization of Discontinuous Fuzzy Delayed Inertial Neural Networks

李岩 Li Yan, Guodong Zhang, Xiao Zhou, Jing Han
article en
2 citations

Abstract

Our goal is addressing the problems of preassigned-time projective synchronization and fixed-time projective synchronization of the system that studied in our article, and fuzzy terms, mixed delays, inertial terms and discontinuous activation function are also included in this system. Further, the results of this article are based on the latest fixed-time stabilization lemma. Through the construction of adaptative Lyapunov function and new corresponding feedback controllers, we arrive at the results of fixed-time projective synchronization and preassigned-time projective synchronization about investigated neural networks, and we are using a non-reduced-order approach. The proposed results are more extensive in comparison with traditional anti-synchronization and synchronization. At the end of our article, to demonstrate the advantage of our outcomes, we display some simulation example.

IEEE AccessVol. 13
Minzu University of China (CN), Huazhong Agricultural University (CN), Wuhan Business University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 8%
Neural Networks Stability and Synchronization
2.97
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