Shunting Inhibitory Cellular Neural Networks with Compartmental Unpredictable Coefficients and Inputs
Akhmet M. Tleubergenova M. Zhamanshin A.
March 2023MDPI
Mathematics
2023#11Issue 6
Shunting inhibitory cellular neural networks with compartmental periodic unpredictable coefficients and inputs is the focus of this research. A new algorithm is suggested, to enlarge the set of known unpredictable functions by applying diagonalization in arguments of functions of several variables. Sufficient conditions for the existence and uniqueness of exponentially stable unpredictable and Poisson stable outputs are obtained. To attain theoretical results, the included intervals method and the contraction mapping principle are used. Appropriate examples with numerical simulations that support the theoretical results are provided. It is shown how dynamics of the neural network depend on a new numerical characteristic, the degree of periodicity.
compartmental periodic unpredictable functions , exponential stability , Poisson stable solutions , shunting inhibitory cellular neural networks , the method of included intervals , unpredictable solutions
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Department of Mathematics, Middle East Technical University, Ankara, 06800, Turkey
Department of Mathematics, 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
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