Dynamics of Hopfield-Type Neural Networks with Modulo Periodic Unpredictable Synaptic Connections, Rates and Inputs
Akhmet M. Tleubergenova M. Zhamanshin A.
November 2022MDPI
Entropy
2022#24Issue 11
In this paper, we rigorously prove that unpredictable oscillations take place in the dynamics of Hopfield-type neural networks (HNNs) when synaptic connections, rates and external inputs are modulo periodic unpredictable. The synaptic connections, rates and inputs are synchronized to obtain the convergence of outputs on the compact subsets of the real axis. The existence, uniqueness, and exponential stability of such motions are discussed. The method of included intervals and the contraction mapping principle are applied to attain the theoretical results. In addition to the analysis, we have provided strong simulation arguments, considering that all the assumed conditions are satisfied. It is shown how a new parameter, degree of periodicity, affects the dynamics of the neural network.
exponential stability , Hopfield-type neural networks , modulo periodic unpredictable synaptic connections , numerical simulations , rates and inputs , unpredictable solutions
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Department of Mathematics, Middle East Technical University, Ankara, 06531, Turkey
Department of Mathematics, Aktobe Regional University, Aktobe, 030000, Kazakhstan
Institute of Information and Computational Technologies CS MES RK, Almaty, 050010, Kazakhstan
Department of Mathematics
Department of Mathematics
Institute of Information and Computational Technologies CS MES RK
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