Robust synchronization of reaction-diffusion memristive neural networks with parameter uncertainties and general couplings.

This study investigates the robust synchronization of coupled reaction-diffusion memristive neural networks with parameter uncertainties, internal time delays, and general coupling configurations. The proposed synchronization approach relaxes restrictive assumptions on coupling structures and parameter consistency, accommodating systems with both excitatory and inhibitory connections, parameter uncertainties, and nonlinear coupling functions, which are common in real-world applications yet rarely addressed in the literature. Using a novel convergence result for a class of differential inequalities, we establish a robust synchronization criterion that is both theoretically rigorous and practically verifiable. Illustrative examples validate the effectiveness of the proposed method, demonstrating its ability to address synchronization problems in models that existing methods cannot handle. This work advances synchronization theory for memristive neural networks, extending its applicability to a broader range of systems.