Executive Summary : | The proposed research focuses on the dynamics of fractional-order Clifford-valued neural networks and their application to image encryption. These networks are designed to handle time delays, stochastic disturbances, impulsive effects, and uncertainties that can affect their behaviors. Clifford-valued NNs are a popular area of research due to their ability to process multilevel information using multistate activation functions and require a limited number of network parameters. Fractional calculus, a natural extension of classical calculus, is widely used for modeling and control processes in various fields. It provides a more accurate representation of physical systems than ordinary calculus, making it of great theoretical and practical significance. The main objective of the proposed research is to design and analyze fractional-order Clifford-valued NNs, extend existing techniques, or introduce a new technique for studying their dynamics. The research also aims to apply the theoretical results of the proposed research to the application of image encryption through mathematical analysis and techniques. Additionally, an image encryption algorithm will be developed based on fractional-order Clifford-valued NNs solutions, providing a more effective method for security measures. In conclusion, the proposed research aims to explore the dynamics of fractional-order Clifford-valued neural networks and their application to image encryption. |