Fixed-Time Synchronization of Neural Networks with Parameter Uncertainties via Quantized Intermittent Control

Author(s):  
Wenqiang Yang ◽  
Junjian Huang ◽  
Xin Wang
Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3086
Author(s):  
Wenqiang Yang ◽  
Li Xiao ◽  
Junjian Huang ◽  
Jinyue Yang

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.


2021 ◽  
Vol 422 ◽  
pp. 295-313 ◽  
Author(s):  
Fanchao Kong ◽  
Quanxin Zhu ◽  
Rathinasamy Sakthivel ◽  
Ardashir Mohammadzadeh

2020 ◽  
Vol 415 ◽  
pp. 74-83
Author(s):  
Fangmin Ren ◽  
Minghui Jiang ◽  
Hao Xu ◽  
Mengqin Li

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