Prescribed-time Quantified Intermittent Control for Stochastic FCNN and a Novel Cryptosystem

This study aims to introduce a user-controlled chaotic fuzzy cellular neural network (FCNN) model that incorporates the effects of stochastic (external) disturbances and proportional delay. Theoretically, synchronization analysis is a considerably more effective approach to exploring the dynamical characteristics of the FCNN model with and without external control input. FCNN models with suitable control input help to possess the dynamical characteristics of traditional FCNN but with the potential to handle stochastic disturbances and other uncertainties. Besides, this study focuses on achieving synchronization within a prescribed-time synchronization (PTS). In this regard, a quantified intermittent control (QIC) scheme is proposed, which is a considerably simple but effective control for nonlinear models with stochastic disturbances. Along with QIC, the synchronization of uncontrolled (drive)-controlled (response) FCNN models can be guaranteed by employing the Lyapunov stability theory, Ito's calculus, and some inequalities. Mathematically, sufficient conditions that guarantee the global asymptotically stability of the error model will ensure the synchronization of the drive-response FCNN model. In terms of application, the drive-response model can be used as a cryptosystem (pseudorandom generator) that helps to encrypt the information from the sender side and to decrypt the information from the receiver side. Due to the chaos and randomness in the solutions, the proposed encryption/decryption algorithm is more effective and resistive than existing algorithms.