Mean square stability of two classes of theta methods for numerical computation and simulation of delayed stochastic Hopfield neural networks

DOI10.1016/j.cam.2018.04.018OpenAlexW2805471816MaRDI QIDQ1643863

Linna Liu, Quanxin Zhu, Fei Qi Deng

Publication date: 20 June 2018

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2018.04.018

zbMATH Keywords

stochastic differential equationtime delayHopfield neural networkmean square stabilitysplit-step theta methodstochastic linear theta method

Mathematics Subject Classification ID

Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Stability and convergence of numerical methods for ordinary differential equations (65L20) Stochastic functional-differential equations (34K50) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30)

Related Items

The balanced split step theta approximations of stochastic neutral Hopfield neural networks with time delay and Poisson jumps ⋮ Stability analysis of split-step theta method for neutral stochastic delayed neural networks

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