Finite-time stability for fractional-order complex-valued neural networks with time delay

Taotao Hu, Zheng He, Xiaojun Zhang and Shouming Zhong

Applied Mathematics and Computation, 2020, vol. 365, issue C

Abstract: This paper explores the finite-time stability of fractional-order complex valued neural networks with time delay. By employing Laplace transform and the properties of Mittag-Leffler function, a lemma of exponent stability is developed to derive the finite-time stability conditions. Further, by using the proposed lemma and the techniques of inequalities, the finite-time stability of fractional-order complex-valued neural networks with time delay is analyzed with and without a controller. In addition, some sufficient conditions are proposed to analyze the finite-time stability of the fractional-order complex-valued neural networks and the setting time for stability is also estimated. Finally, two examples are used to verify the validity and feasibility of the proposed criteria.

Keywords: Finite-time stability; Fractional-order; Complex-valued neural networks; Mittag-Leffler function; Time delay (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:365:y:2020:i:c:s0096300319307076

DOI: 10.1016/j.amc.2019.124715

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