Global Dissipativity and Quasi-Synchronization of Fractional-Order Complex-Valued Neural Networks With Mixed Delays

This article presents a rigorous investigation of the global dissipativity (GD) and quasi-synchronization (QS) of fractional-order complex-valued neural networks (FOCVNNs) with mixed delays and external disturbances. The considered mixed delays consist of both time-varying delays (TVDs) and infinite distributed delays (IDDs), which significantly complicate the dynamical analysis of fractional-order systems. To address this challenge, the fractional comparison principle is first extended to accommodate IDD. Based on this extension, a generalized fractional Halanay inequality is established, providing an effective analytical tool for fractional-order systems with mixed delays. By employing the proposed inequality, two sufficient conditions are derived to guarantee GD and QS of FOCVNNs with mixed delays and external disturbances under designed feedback control. Finally, the effectiveness and applicability of the theoretical analysis are demonstrated through two illustrative examples, including a standard numerical simulation and a secure communication scheme constructed from a fractional-order chaotic system with mixed delays.