New finite-time passivity criteria for delayed fractional-order neural networks based on Lyapunov function approach

M. Shafiya and G. Nagamani

Chaos, Solitons & Fractals, 2022, vol. 158, issue C

Abstract: This paper deals with the problem of finite-time passivity analysis for a class of fractional-order neural networks with constant time delay. Firstly, based on the existing passivity definition, some new concepts namely, finite-time passivity, finite-time input strict passivity, finite-time output strict passivity, and finite-time strict passivity are introduced in terms of Lyapunov function for fractional-order neural networks. In this paper, for the first time, by defining an appropriate controller and by exploiting the introduced definitions, some novel delay-dependent and order-dependent sufficient conditions ensuring the passivity performances are obtained for the addressed system. In addition, the finite-time stability conditions are also presented with an explicit formula for determining the value of setting time for stability. Finally, one numerical example is given to verify the effectiveness of the obtained theoretical results and the simulation results are provided for better understanding of the proposed problem.

Keywords: Fractional-order neural networks; Finite-time passivity; Finite-time stability; Lyapunov stability theory; Linear matrix inequality (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:158:y:2022:i:c:s0960077922002156

DOI: 10.1016/j.chaos.2022.112005

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