New approach to finite-time stability for fractional-order BAM neural networks with discrete and distributed delays

Feifei Du and Jun-Guo Lu

Chaos, Solitons & Fractals, 2021, vol. 151, issue C

Abstract: The finite-time stability (FTS) for fractional-order bidirectional associative memory neural networks (FOBAMNNs) with discrete and distributed delays is investigated in this article. Firstly, a novel fractional-order delayed Gronwall inequality (FODGI) with two different orders is developed. As a straightforward generalization, a novel generalized FODGI with multiple different orders is also established, which can be applied to investigate the stability of fractional-order delayed systems (FODSs) with multiple different orders. Secondly, based on the former inequality, two novel FTS criteria of FOBAMNNs with the orders 0<℘<1 and 1<℘<2 are established. Finally, three examples are exhibited to illustrate the effectiveness and the less conservativeness of the proposed methods.

Keywords: Fractional-order; Finite-time stability; Bidirectional associative memory neural network; Gronwall inequality; Distributed delay (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:151:y:2021:i:c:s0960077921005798

DOI: 10.1016/j.chaos.2021.111225

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