Alpha Unpredictable Cohen–Grossberg Neural Networks with Poisson Stable Piecewise Constant Arguments
Akhmet M. Nugayeva Z. Seilova R.
April 2025Multidisciplinary Digital Publishing Institute (MDPI)
Mathematics
2025#13Issue 7
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.
alpha unpredictable neural networks , alpha unpredictable oscillation , Cohen–Grossberg type neural networks , method of included intervals , method of pseudo-quasilinear reduction , Poisson stable piecewise constant argument , ultra Poincare chaos
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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
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