A fractional Halanay inequality for neutral systems and its application to Cohen-Grossberg neural networks

What is it about?

We expand the Halanay inequality to accommodate fractional-order systems incorporating both discrete and distributed neutral delays. By establishing specific conditions, we demonstrate that the solutions of these systems converge to zero at a Mittag-Leffler rate. Our analysis is versatile, accommodating a wide range of delay kernels. This versatility extends the applicability of our findings to fractional Cohen-Grossberg neural networks, offering valuable insights into their stability and dynamical behavior.

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Why is it important?

Such problems arise across a wide range of fields, including parallel computing, cryptography, image processing, combinatorial optimization, signal theory, and geology.