Exponential Stability of a Class of Neutral Inertial Neural Networks with Multi-Proportional Delays and Leakage Delays

Chao Wang, Yinfang Song (), Fengjiao Zhang and Yuxiao Zhao
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Chao Wang: School of Information and Mathematics, Yangtze University, Jingzhou 430023, China
Yinfang Song: School of Information and Mathematics, Yangtze University, Jingzhou 430023, China
Fengjiao Zhang: School of Information and Mathematics, Yangtze University, Jingzhou 430023, China
Yuxiao Zhao: School of Mathematics and Information Science, Shandong Technology and Business University, Yantai 264005, China

Mathematics, 2023, vol. 11, issue 12, 1-14

Abstract: This paper investigates the exponential stability of a class of neutral inertial neural networks with multi-proportional delays and leakage delays. By utilizing the Lyapunov stability theory, the approach of parametric variation, and the differential inequality technique, some criteria are acquired that can guarantee that all solutions of the addressed system converge exponentially to the equilibrium point. In particular, the neutral term, multi-proportional delays, and leakage delays are incorporated simultaneously, resulting in a more general model, and the findings are novel and refine the previous works. Finally, one example is provided to indicate that the dynamic behavior is consistent with the theoretical analysis.

Keywords: inertial neural networks; proportional delays; leakage delays; exponential convergence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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