Stochastic Dynamics of Discrete-Time Fuzzy Random BAM Neural Networks with Time Delays

Sufang Han, Tianwei Zhang and Guoxin Liu

Mathematical Problems in Engineering, 2019, vol. 2019, 1-20

Abstract:

By using the semidiscrete method of differential equations, a new version of discrete analogue of stochastic fuzzy BAM neural networks was formulated, which gives a more accurate characterization for continuous-time stochastic neural networks than that by the Euler scheme. Firstly, the existence of the - th mean almost periodic sequence solution of the discrete-time stochastic fuzzy BAM neural networks is investigated with the help of Minkowski inequality, Hölder inequality, and Krasnoselskii’s fixed point theorem. Secondly, the - th moment global exponential stability of the discrete-time stochastic fuzzy BAM neural networks is also studied by using some analytical skills in stochastic theory. Finally, two examples with computer simulations are given to demonstrate that our results are feasible. The main results obtained in this paper are completely new, and the methods used in this paper provide a possible technique to study - th mean almost periodic sequence solution and - th moment global exponential stability of semidiscrete stochastic fuzzy models.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:9416234

DOI: 10.1155/2019/9416234

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