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

Author

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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)

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.

Suggested Citation

  • Wenqiang Yang & Li Xiao & Junjian Huang & Jinyue Yang, 2021. "Fixed-Time Synchronization of Neural Networks Based on Quantized Intermittent Control for Image Protection," Mathematics, MDPI, vol. 9(23), pages 1-14, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:23:p:3086-:d:691724
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    References listed on IDEAS

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    1. Shuai Liu & Chuan Chen & Haipeng Peng, 2020. "Fixed-Time Synchronization of Neural Networks with Discrete Delay," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-9, August.
    2. Mazloom, Sahar & Eftekhari-Moghadam, Amir Masud, 2009. "Color image encryption based on Coupled Nonlinear Chaotic Map," Chaos, Solitons & Fractals, Elsevier, vol. 42(3), pages 1745-1754.
    3. Nguyen, Duy-Minh & Bahri, Imen & Krebs, Guillaume & Berthelot, Eric & Marchand, Claude, 2019. "Vibration study of the intermittent control for a switched reluctance machine," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 158(C), pages 308-325.
    4. Tutueva, Aleksandra V. & Nepomuceno, Erivelton G. & Karimov, Artur I. & Andreev, Valery S. & Butusov, Denis N., 2020. "Adaptive chaotic maps and their application to pseudo-random numbers generation," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
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    Cited by:

    1. Zhen Yang & Zhengqiu Zhang, 2023. "New Results on Finite-Time Synchronization of Complex-Valued BAM Neural Networks with Time Delays by the Quadratic Analysis Approach," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    2. Lu Pang & Cheng Hu & Juan Yu & Haijun Jiang, 2022. "Fixed-Time Synchronization for Fuzzy-Based Impulsive Complex Networks," Mathematics, MDPI, vol. 10(9), pages 1-16, May.

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