Stability analysis of stochastic fractional-order competitive neural networks with leakage delay

This article, we explore the stability analysis of stochastic fractional-order competitive neural networks with leakage delay. The main objective of this paper is to establish a new set of sufficient conditions, which is for the uniform stability in mean square of such stochastic fractional-order neural networks with leakage. Specifically, the presence and uniqueness of arrangements and stability in mean square for a class of stochastic fractional-order neural systems with delays are concentrated by using Cauchy-Schwartz inequality, Burkholder-Davis-Gundy inequality, Banach fixed point principle and stochastic analysis theory, respectively. Finally, four numerical recreations are given to confirm the hypothetical discoveries.

Automated summary

This article investigates the stability analysis of stochastic fractional-order competitive neural networks with leakage delay, aiming to establish new sufficient conditions for uniform stability in mean square. The presence and uniqueness of arrangements and stability in mean square for a class of stochastic fractional-order neural systems with delays are concentrated using various mathematical inequalities and principles, along with stochastic analysis theory. Four numerical recreations are presented to validate the theoretical findings.