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Memory sampled data control for switched-type neural networks and its application in image secure communications

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  • Cao, Yang
  • Udhayakumar, K.
  • Veerakumari, K. Pradeepa
  • Rakkiyappan, R.

Abstract

The present study focuses on designing the memory-based sampled data control approach to achieve the desired performance from switched-type of neural networks. The overall contribution of the study can be viewed from two directions. The first part is to derive the theoretical-based sufficient stability conditions that ensure the synchronization of uncontrolled and controlled systems. The idea behind the synchronization analysis is because of time complexity and computational cost. Once, we derive the closed-loop system then user can employ them in any practical application without interruptions. The existence of time-delays during the information transmission is inevitable, and in a practical sense, the delays, which are a function of time are well-effective. Besides, the sufficient conditions are derived in terms of solvable linear matrix inequalities (LMIs), which can be solved to ensure the global asymptotical stability of the error system, derived from uncontrolled–controlled systems. Technically, the work integrates with the new type of discontinuous Lyapunov–Krasovskii functional candidate with integral terms, several delay-dependent stability conditions, and Wirtinger-based integral inequality. The second part works on utilizing the controlled system into a real-life application, say, encryption/decryption process. To an evident, the proposed controlled system is considered as crypto-system in the encryption/decryption process.

Suggested Citation

  • Cao, Yang & Udhayakumar, K. & Veerakumari, K. Pradeepa & Rakkiyappan, R., 2022. "Memory sampled data control for switched-type neural networks and its application in image secure communications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 564-587.
    • Handle: RePEc:eee:matcom:v:201:y:2022:i:c:p:564-587
    DOI: 10.1016/j.matcom.2021.03.021
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    References listed on IDEAS

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    1. Luo, Mengzhuo & Cheng, Jun & Liu, Xinzhi & Zhong, Shouming, 2019. "An extended synchronization analysis for memristor-based coupled neural networks via aperiodically intermittent control," Applied Mathematics and Computation, Elsevier, vol. 344, pages 163-182.
    2. Liu, Yamin & Xuan, Zuxing & Wang, Zhen & Zhou, Jianping & Liu, Yajuan, 2020. "Sampled-data exponential synchronization of time-delay neural networks subject to random controller gain perturbations," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    3. Ge, Chao & Wang, Bingfang & Wei, Xian & Liu, Yajuan, 2017. "Exponential synchronization of a class of neural networks with sampled-data control," Applied Mathematics and Computation, Elsevier, vol. 315(C), pages 150-161.
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    Cited by:

    1. Priyanka, K. Sri Raja & Soundararajan, G. & Kashkynbayev, Ardak & Nagamani, G., 2023. "Exponential H∞ synchronization and anti-synchronization of delayed discrete-time complex-valued neural networks with uncertainties," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 301-321.

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