ANTI-PERIODIC SYNCHRONIZATION FOR NABLA QUATERNION-VALUED COHEN-GROSSBERG NEURAL NETWORKS WITH TIME-VARYING DELAYS AND IMPULSIVE EFFECTS ON TIME SCALES

This paper aims to consider a class of Nabla quaternion-valued Cohen-Grossberg neural networks with time-varying delays and impulsive effects on time scales. By employing a continuation theorem of coincidence degree and calculus theory on time scales, we first establish a novel analytical framework for anti-periodic solutions to such networks. Secondly, by constructing appropriate Lyapunov functions and designing state feedback and impulsive controllers, some sufficient conditions are derived to ensure the global exponential synchronization of the response system and the driving system. The proposed results are extensions and supplements of existing findings in the field. Finally, a numerical example is given to verify the feasibility of the main results.