exponential stability

networks time-varying

neural networks time-varying

delay systems

takagi sugeno

complex dynamical

time-varying delays

cellular neural networks

linear matrix

projective synchronization

Neurocomputing

This paper is devoted to researching the robust stability of fractional-order complex-valued projective neural networks (FOCVPNNs) with neutral-type delays and uncertain parameters. Without dividing the FOCVPNNs into two real-valued systems, based on Lyapunov method, matrix inequality technique and homeomorphism principle, several delay-independent and delay-dependent criteria are established to make sure that the equilibrium point of the addressed FOCVPNNs is existent, unique and globally robustly stable. Finally, three examples with simulations are given to verify the availability of the main results.

Robust stability for a class of fractional-order complex-valued projective neural networks with neutral-type delays and uncertain parameters