Open AccessMean boundedness, global attractivity and almost periodic sequence of stochastic neural networks with discrete-time analogue
Author(s) -
Sumei Sun,
Yanhong Li
Publication year - 2021
Publication title -
filomat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.449
H-Index - 34
eISSN - 2406-0933
pISSN - 0354-5180
DOI - 10.2298/fil2112919s
Subject(s) - mathematics , sequence (biology) , artificial neural network , class (philosophy) , stochastic process , discrete time and continuous time , stochastic neural network , recurrent neural network , artificial intelligence , statistics , computer science , genetics , biology
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.