Robust stability of fractional-order quaternion-valued neural networks with neutral delays and parameter uncertainties

This paper focuses on the robust stability analysis of fractional-order quaternion-valued neural networks (FOQVNNs) with neutral delay and parameter uncertainties. Without transforming the FOQVNNs into equivalent two complex-valued systems or four real-valued systems, based on homeomorphism principle, matrix inequality technique and Lyapunov method, both delay-independent and delay-dependent criteria to guarantee the existence, uniqueness and global stability of equilibrium point for the considered FOQVNNs are derived in the form of linear matrix inequality (LMI). Two examples with simulations are provided to manifest the theoretical results. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This study investigated the robust stability analysis of FOQVNNs with neutral delay and parameter uncertainties, deriving delay-independent and delay-dependent criteria using matrix inequality technique and Lyapunov method to guarantee the existence, uniqueness, and global stability of equilibrium point for FOQVNNs. Two simulation examples were provided to demonstrate the theoretical results.