Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics
Akhmet M. Tleubergenova M. Zhamanshin A.
January 2024Elsevier Ltd
Chaos, Solitons and Fractals
2024#178
In this paper, we provide theoretical as well as numerical results concerning recurrent oscillations in Cohen-Grossberg 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.
Cohen-Grossberg neural networks , Compartmental periodic unpredictable inputs and strengths of connectivity , Exponential stability , Numerical simulations , Unpredictable and Poisson stable inputs and strengths of connectivity , Unpredictable and Poisson stable outputs
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Department of Mathematics, Middle East Technical University, Ankara, 06800, Turkey
Department of Mathematics, K. Zhubanov Aktobe Regional University, Aktobe, 030000, Kazakhstan
Institute of Information and Computational Technologies, Almaty, 050010, Kazakhstan
Department of Mathematics
Department of Mathematics
Institute of Information and Computational Technologies
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026