Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics

被引:2
作者
Akhmet, Marat [1 ]
Tleubergenova, Madina [2 ,3 ]
Zhamanshin, Akylbek [1 ]
机构
[1] Middle East Tech Univ, Dept Math, TR-06800 Ankara, Turkiye
[2] K Zhubanov Aktobe Reg Univ, Dept Math, Aktobe 030000, Kazakhstan
[3] Inst Informat & Computat Technol, Alma Ata 050010, Kazakhstan
关键词
Cohen-Grossberg neural networks; Unpredictable and Poisson stable inputs and; strengths of connectivity; Unpredictable and Poisson stable outputs; Compartmental periodic unpredictable inputs; and strengths of connectivity; Exponential stability; Numerical simulations; ALMOST-PERIODIC SOLUTION; EXPONENTIAL STABILITY; PATTERN-RECOGNITION; EXISTENCE; CHAOS;
D O I
10.1016/j.chaos.2023.114307
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
In this paper, we provide theoretical as well as numerical results concerning recurrent oscillations in CohenGrossberg neural networks with variable inputs and strengths of connectivity for cells, which are unpredictable or Poisson stable functions. A special case of the compartmental coefficients with periodic and unpredictable ingredients is also carefully researched. By numerical and graphical analysis, it is shown how a constructive technical characteristic, the degree of periodicity, reflects contributions of the ingredients to final outputs of the neural networks. Sufficient conditions are obtained to guarantee the existence of exponentially stable unpredictable outputs of the models. They are specified for Poisson stability by utilizing the original method of included intervals. Examples with numerical simulations that support the theoretical results are provided.
引用
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页数:10
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