On stability and event trigger control of fractional neural networks by fractional non-autonomous Halanay inequalities

Feng-Xian Wang, Jie Zhang, Yan-Jun Shu and Xin-Ge Liu

Chaos, Solitons & Fractals, 2023, vol. 170, issue C

Abstract: This paper studies the stability and control of fractional neural networks by Halanay inequality technique. Based on the fractional comparison principle and supremum and infimum principle, a novel fractional non-autonomous Halanay inequality is developed. The fractional non-autonomous Halanay inequality is in a form of integral, which considers the global nature of the system parameters and reduces estimation error. By combining the Halanay inequality with a maximum auxiliary function, an asymptotically stable discriminant condition for fractional Hopfield time-delay neural networks is established in an algebraic form. Moreover, event trigger control for fractional neural networks is studied. Low network bandwidth costs and high control efficiency are guaranteed by a Mittag-Leffler type event-triggered mechanism. Then, a discriminant condition on the event trigger control for fractional neural networks is established. The advantages of the proposed methods are demonstrated by three numerical examples.

Keywords: Fractional Halanay inequality; Neural networks; Asymptotic stability; Event trigger control (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:170:y:2023:i:c:s0960077923003193

DOI: 10.1016/j.chaos.2023.113418

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