On a heavy-tailed distribution and the stability of an equilibrium in a distributed delay symmetric network

Israel Ncube

Chaos, Solitons & Fractals, 2021, vol. 152, issue C

Abstract: We consider a static artificial neural network model endowed with multiple unbounded S-type distributed time delays. The delay kernels are described by the Pareto distribution, which is a heavy-tailed power-law probability distribution frequently employed in the characterisation of many observable phenomena. We give a characterisation of the effects of the shape and the scale of the Pareto delay distribution on the stability of an equilibrium of the network.

Keywords: Static neural networks; S-type distributed time delays; Pareto distribution; Equilibrium; Stability (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:152:y:2021:i:c:s0960077921006846

DOI: 10.1016/j.chaos.2021.111330

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