Finite-Time Projective Synchronization in Fractional-Order Inertial Memristive Neural Networks: A Novel Approach to Image Encryption
Huixian Weng, Yongqing Yang, Rixu Hao, Fengyi Liu · Fractal and Fractional · 2024
This paper presents a novel finite-time projective synchronization (FTPS) control strategy for fractional-order inertial memristive neural networks (FOIMNNs), exploring its application in image encryption. A sufficient condition for ensuring FTPS in FOIMNNs is established and validated through numerical simulations. These simulations indicate that the proposed strategy provides reliable synchronization performance. Furthermore, an efficient method for image encryption was developed, potentially improving data security. Comparative analyses with existing methods suggest that this approach could offer incremental benefits in secure communication and data protection.