Hybrid-Impulses-Based Control for Exponential Stability of Inertial Delayed Neural Networks Using Average Impulsive Gain Strategy

• Published in: Asian Control Conference (Online) pp. 476 - 481
• Main Authors: Dong, Shiyu, Zhu, Hong, Shi, Kaibo, Zhong, Shouming, Zhang, Yuping
• Format: Conference Proceeding
• Language: English
• Published: ACA 04.05.2022
• Subjects: average impulsive gain; Control theory; Differential equations; exponential stability; hybrid impulses; Inertial delayed neural networks; Neural networks; Stability; Sufficient conditions
• ISSN: 2770-8373
• DOI: 10.23919/ASCC56756.2022.9828188

This work investigates the exponential stability problem of inertial delayed neural networks (IDNNs) with hybrid impulses by taking advantage of the concepts of average impulsive interval and average impulsive gain. Firstly, the second-order differential system can be transformed into two first-order differential equations by using the variable substitution method. Secondly, the impulsive sequence involves stabilizing impulses and destabilizing impulses simultaneously. Furthermore, both the cases of T a <∞ and T a =∞ are considered. Then, based on the approach of comparison principle, some sufficient conditions on exponential stability of IDNNs subject to hybrid impulses are established. Finally, two numerical examples are shown to verify the effectiveness of the theoretical results.