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Keywords = method of pseudo-quasilinear reduction

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27 pages, 597 KB  
Article
Alpha Unpredictable Cohen–Grossberg Neural Networks with Poisson Stable Piecewise Constant Arguments
by Marat Akhmet, Zakhira Nugayeva and Roza Seilova
Mathematics 2025, 13(7), 1068; https://doi.org/10.3390/math13071068 - 25 Mar 2025
Viewed by 453
Abstract
There are three principal novelties in the present investigation. It is the first time Cohen–Grossberg-type neural networks are considered with the most general delay and advanced piecewise constant arguments. The model is alpha unpredictable in the sense of electrical inputs and is researched [...] Read more.
There are three principal novelties in the present investigation. It is the first time Cohen–Grossberg-type neural networks are considered with the most general delay and advanced piecewise constant arguments. The model is alpha unpredictable in the sense of electrical inputs and is researched under the conditions of alpha unpredictable and Poisson stable outputs. Thus, the phenomenon of ultra Poincaré chaos, which can be indicated through the analysis of a single motion, is now confirmed for a most sophisticated neural network. Moreover, finally, the approach of pseudo-quasilinear reduction, in its most effective form is now expanded for strong nonlinearities with time switching. The complexity of the discussed model makes it universal and useful for various specific cases. Appropriate examples with simulations that support the theoretical results are provided. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Complex Networks)
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