Robust Stability Analysis of Fractional-Order Hopfield Neural Networks with Parameter Uncertainties

Abstract EN

The issue of robust stability for fractional-order Hopfield neural networks with parameter uncertainties is investigated in this paper.

For such neural system, its existence, uniqueness, and global Mittag-Leffler stability of the equilibrium point are analyzed by employing suitable Lyapunov functionals.

Based on the fractional-order Lyapunov direct method, the sufficient conditions are proposed for the robust stability of the studied networks.

Moreover, robust synchronization and quasi-synchronization between the class of neural networks are discussed.

Furthermore, some numerical examples are given to show the effectiveness of our obtained theoretical results.