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Open AccessArticle
Global Exponential Stability of Impulsive Delayed Neural Networks with Parameter Uncertainties and Reaction–Diffusion Terms
by
Fei Luo
Fei Luo
Dr. Fei Luo holds a BS from the School of Mathematics and Computer Science, Chongqing University of [...]
Dr. Fei Luo holds a BS from the School of Mathematics and Computer Science, Chongqing University of International Business and Economics, an MS from the School of Mathematical Sciences, Chongqing Normal University, and a PhD from the School of Mathematics, Sichuan University. He was a teaching assistant at the College of Mathematics and Statistics, Sichuan University of Science and Engineering, and is now a lecturer. His research areas are topological dynamical systems, differential equations and dynamical systems, and stability and synchronization of complex network systems. He has taught the following courses: Advanced Mathematics, Elementary Number Theory, Market Research, Functional Analysis, Ordinary Differential Equations, and Linear Algebra. He has presided over one Sichuan Provincial Key Laboratory of Bridge Nondestructive Testing and Engineering Computing Open Fund Project (ranked fourth) and two National Natural Science Foundation of China general projects (ranked fourth and fifth). He is currently presiding over one Sichuan Provincial Key Laboratory of University Open Fund Project.
Weiyi Hu
Weiyi Hu
Enli Wu
Enli Wu
Xiufang Yuan
Xiufang Yuan
College of Mathematics and Statistics, Sichuan University of Science and Engineering, Zigong 643000, China
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Author to whom correspondence should be addressed.
Mathematics 2024, 12(15), 2395; https://doi.org/10.3390/math12152395 (registering DOI)
Submission received: 3 July 2024
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Revised: 25 July 2024
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Accepted: 30 July 2024
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Published: 31 July 2024
Abstract
In this paper, we present a method to achieve exponential stability in a class of impulsive delayed neural networks containing parameter uncertainties, time-varying delays, and impulsive effect and reaction–diffusion terms. By using an integro-differential inequality with impulsive initial conditions and employing the M-matrix theory and the nonlinear measure approach, some new sufficient conditions ensuring the global exponential stability and global robust exponential stability of the considered system are derived. In particular, the results obtained are presented by simple algebraic inequalities, which are certainly more concise than the previous methods. By comparisons and examples, it is shown that the results obtained are effective and useful.
Share and Cite
MDPI and ACS Style
Luo, F.; Hu, W.; Wu, E.; Yuan, X.
Global Exponential Stability of Impulsive Delayed Neural Networks with Parameter Uncertainties and Reaction–Diffusion Terms. Mathematics 2024, 12, 2395.
https://doi.org/10.3390/math12152395
AMA Style
Luo F, Hu W, Wu E, Yuan X.
Global Exponential Stability of Impulsive Delayed Neural Networks with Parameter Uncertainties and Reaction–Diffusion Terms. Mathematics. 2024; 12(15):2395.
https://doi.org/10.3390/math12152395
Chicago/Turabian Style
Luo, Fei, Weiyi Hu, Enli Wu, and Xiufang Yuan.
2024. "Global Exponential Stability of Impulsive Delayed Neural Networks with Parameter Uncertainties and Reaction–Diffusion Terms" Mathematics 12, no. 15: 2395.
https://doi.org/10.3390/math12152395
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