Geodesic
Mean boundedness, global attractivity and almost periodic sequence of stochastic neural networks with discrete-time analogue
Filomat, Tome 35 (2021) no. 12, p. 3919

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A class of stochastic neural networks with discrete-time analogue is investigated in this paper. By employing contraction mapping principle and some stochastic analysis techniques, we establish some sufficient conditions for mean boundedness, global attractivity and almost periodic sequence of the model. An example and graphic illustrations are displayed to visually expound the main contributions. The research techniques in this literature are suitable for other stochastic models in science and engineering
DOI : 10.2298/FIL2112919S
Classification : 34C27
Keywords: stochastic, neural networks, exponential stability, contraction mapping principle 
Shumin Sun; Yanhong Li. Mean boundedness, global attractivity and almost periodic sequence of stochastic neural networks with discrete-time analogue. Filomat, Tome 35 (2021) no. 12, p. 3919 . doi: 10.2298/FIL2112919S
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     title = {Mean boundedness, global attractivity and almost periodic sequence of stochastic neural networks with discrete-time analogue},
     journal = {Filomat},
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     year = {2021},
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     doi = {10.2298/FIL2112919S},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.2298/FIL2112919S/}
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