Improved Stability Criteria for Delayed Neural Networks via a Relaxed Delay-Product-Type Lapunov–Krasovskii Functional

Shuoting Wang, Kaibo Shi and Jin Yang
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Shuoting Wang: School of Computer, Chengdu University, Chengdu 610106, China
Kaibo Shi: School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China
Jin Yang: School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China

Mathematics, 2022, vol. 10, issue 15, 1-14

Abstract: In this paper, the asymptotic stability problem of neural networks with time-varying delays is investigated. First, a new sufficient and necessary condition on a general polynomial inequality was developed. Then, a novel augmented Lyapunov–Krasovskii functional (LKF) was constructed, which efficiently introduces some new terms related to the previous information of neuron activation function. Furthermore, based on the suitable LKF and the stated negative condition of the general polynomial, two criteria with less conservatism were derived in the form of linear matrix inequalities. Finally, two numerical examples were carried out to confirm the superiority of the proposed criteria, and a larger allowable upper bound of delays was achieved.

Keywords: neural networks; asymptotic stability; polynomial inequalities; time-varying delays; Lyapunov–Krasovskii functional (LKF) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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