Fixed-Time Synchronization of Neural Networks Based on Quantized Intermittent Control for Image Protection

Abstract

This paper considers the fixed-time synchronization (FIXTS) of neural networks (NNs) by using quantized intermittent control (QIC). Based on QIC, a fixed-time controller is designed to ensure that the NNs achieve synchronization in finite time. With this controller, the settling time can be estimated regardless of initial conditions. After ensuring that the system has stabilized through this strategy, it is suitable for image protection given the behavior of the system. Meanwhile, the encryption effect of the image depends on the encryption algorithm, and the quality of the decrypted image depends on the synchronization error of NNs. The numerical results show that the designed controller is effective and validate the practical application of FIXTS of NNs in image protection.

Fixed/Preassigned-Time Synchronization of Fully Quaternion-Valued Cohen–Grossberg Neural Networks with Generalized Time Delay

“…Theorem 4. Based on Assumptions 1-3 and the controller (31), if conditions (19) and ( 20) hold, then the networks (1) and ( 2) achieve PET synchronization within 0 < T pet ≤ T.…”

“…In order to address this challenge, the concept of fixed time (FIT) was introduced and the FIT stability theorem was first proposed in [17], in which the estimate of the ST is improved by eliminating its dependence on initial values. Since then, numerous remarkable research endeavors have emerged, encompassing FIT synchronization of recurrent neural networks [18][19][20], complex-valued neural networks (CVNNs) [21][22][23], and QVNNs [24][25][26][27][28]. In [24][25][26], the FIT synchronization of QVNNs was investigated by decomposing the QVNN model into four real valued neural networks, which resulted in four real-valued controllers being designed.…”

Fixed/Preassigned-Time Synchronization of Fuzzy Memristive Fully Quaternion-Valued Neural Networks Based on Event-Triggered Control

“…Hu et al have proposed a mature theory of prescribed-time stability [25], which successfully extended the fixed-time stability theory. Subsequently, numerous noteworthy achievements have been made [26][27][28][29][30]. The preassigned-time synchronization (PETS) of complex-valued memristive neural networks was studied in [29].…”