Fixed-Time Synchronization of Neural Networks Based on Quantized Intermittent Control for Image Protection
Wenqiang Yang,
Li Xiao,
Junjian Huang and
Jinyue Yang
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Wenqiang Yang: Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Li Xiao: Key Laboratory of Machine Perception and Children’s Intelligence Development, Chongqing University of Education, Chongqing 400067, China
Junjian Huang: Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Jinyue Yang: Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Mathematics, 2021, vol. 9, issue 23, 1-14
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.
Keywords: fixed-time synchronization; quantized intermittent control; image protection; neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)
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