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Cited by 280Neural Networks Stability and SynchronizationStability and Control of Uncertain SystemsChaos control and synchronization

The DOI matches its Crossref record.

Impulsive Synchronization of Reaction–Diffusion Neural Networks With Mixed Delays and Its Application to Image Encryption

Chen, Wu-Hua, Luo, Shixian, Zheng, Wei Xing·2016·IEEE Transactions on Neural Networks and Learning Systems
DOI 10.1109/tnnls.2015.2512849
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Abstract

This paper presents a new impulsive synchronization criterion of two identical reaction-diffusion neural networks with discrete and unbounded distributed delays. The new criterion is established by applying an impulse-time-dependent Lyapunov functional combined with the use of a new type of integral inequality for treating the reaction-diffusion terms. The impulse-time-dependent feature of the proposed Lyapunov functional can capture more hybrid dynamical behaviors of the impulsive reaction-diffusion neural networks than the conventional impulse-time-independent Lyapunov functions/functionals, while the new integral inequality, which is derived from Wirtinger's inequality, overcomes the conservatism introduced by the integral inequality used in the previous results. Numerical examples demonstrate the effectiveness of the proposed method. Later, the developed impulsive synchronization method is applied to build a spatiotemporal chaotic cryptosystem that can transmit an encrypted image. The experimental results verify that the proposed image-encrypting cryptosystem has the advantages of large key space and high security against some traditional attacks.

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Crossref · 10.1109/tnnls.2015.2512849

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  • The approach of Dridi et al. uses an extensive key generation mechanism in addition to a neural network and logistic map, which significantly increases its algorithm processing time.

    “The approach of Dridi et.al. uses an extensive key generation mechanism apart from using neural network and logistic map which significantly increases algorithm processing time [55].”

    From A Faster and Robust Artificial Neural Network Based Image Encryption Technique With Improved SSIM (2024) · cites this paper as [55]

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Research works printed but not linked: 46

  1. 10.1109/TFUZZ.2012.2217147 · DOI
  2. 10.1080/00207721.2012.670305 · DOI
  3. hu, 2010, Impulsive control and synchronization for delayed neural networks with reaction–diffusion terms, IEEE Trans Neural Netw, 21, 67, 10.1109/TNN.2009.2034318 · DOI
  4. gan, 2014, Exponential synchronization for reaction-diffusion neural networks with mixed time-varying delays via periodically intermittent control, Nonlinear Anal Model Control, 19, 1, 10.15388/NA.2014.1.1 · DOI
  5. 10.1016/j.neunet.2012.02.038 · DOI
  6. 10.1109/TCYB.2013.2283308 · DOI
  7. 10.1109/TCSI.2008.2006210 · DOI
  8. 10.1016/j.neucom.2012.11.006 · DOI
  9. 10.1137/120897341 · DOI
  10. 10.1016/j.neucom.2010.05.003 · DOI
  11. 10.1016/j.automatica.2010.04.005 · DOI
  12. 10.1109/TNNLS.2014.2322499 · DOI
  13. 10.1109/TNN.2010.2101081 · DOI
  14. 10.1109/TCSI.2011.2163969 · DOI
  15. 10.1109/81.473564 · DOI
  16. 10.1007/s10470-005-5749-x · DOI
  17. 10.1016/j.amc.2008.10.002 · DOI
  18. 10.1016/j.nonrwa.2007.04.003 · DOI
  19. 10.1016/j.chaos.2007.05.002 · DOI
  20. 10.1016/j.apm.2008.11.017 · DOI
  21. 10.1016/j.cam.2010.05.007 · DOI
  22. 10.1007/s11071-011-0138-8 · DOI
  23. 10.1016/S0096-3003(01)00318-6 · DOI
  24. 10.1016/j.cnsns.2011.02.019 · DOI
  25. 10.1016/S0960-0779(99)00192-7 · DOI
  26. 10.1109/TCSI.2009.2016636 · DOI
  27. 10.1016/j.chaos.2007.08.047 · DOI
  28. 10.1109/81.633877 · DOI
  29. 10.1109/81.633887 · DOI
  30. 10.1088/0951-7715/22/3/004 · DOI
  31. 10.1142/S0218127492000562 · DOI
  32. 10.1109/TCSI.2009.2037848 · DOI
  33. 10.1103/PhysRevLett.64.821 · DOI
  34. 10.1016/j.cnsns.2012.08.032 · DOI
  35. 10.1109/TNN.2009.2033910 · DOI
  36. hardy, 1954, Inequalities
  37. 10.1016/j.matcom.2011.11.007 · DOI
  38. 10.1109/TSMCB.2010.2043095 · DOI
  39. 10.1016/j.camwa.2006.03.022 · DOI
  40. 10.1016/j.camwa.2006.05.013 · DOI
  41. huang, 2011, Semigroups of Operators: Theory and Applications
  42. 10.1016/j.physleta.2014.10.019 · DOI
  43. 10.1016/j.automatica.2013.05.030 · DOI
  44. 10.1016/j.mcm.2009.05.038 · DOI
  45. liu, 1999, Nonlinear impulsive evolution equations, Dyna Continuous Discrete Impul Syst, 6, 77
  46. 10.1016/j.physleta.2007.04.079 · DOI

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Metadata

Authors
Chen, W.-H., Luo, S., & Zheng, W. X.
Affiliations
  • Guangxi University (CN)
  • Western Sydney University (AU)
ORCID
Venue
IEEE Transactions on Neural Networks and Learning Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2016-12
Volume
27
Issue
12
Pages
2696-2710
ISSN
  • 2162-237X (ISSN-L)
  • 2162-2388
DOI
10.1109/tnnls.2015.2512849
OpenAlex
W2259261597
Cited by
280
References
0
Citations by year
2026: 18 · 2025: 23 · 2024: 27 · 2023: 34 · 2022: 33 · 2021: 34 · 2020: 35 · 2019: 29 · 2018: 22 · 2017: 21 · 2016: 4
FWCI
23.90
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Natural Science Foundation of China (61573111)
  • Guangxi Natural Science Foundation (2013GXNSFDA019003, 2015GXNSFAA139003)
  • Innovation Project of Guangxi Graduate Education (YCSZ2015031)
  • Australian Research Council (DP120104986)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Neural Networks Stability and Synchronization
  • Physical Sciences › Engineering › Control and Systems Engineering › Stability and Control of Uncertain Systems
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Chaos control and synchronization
Keywords
impulsive synchronization, reaction-diffusion neural networks, mixed delays, distributed delay, discrete delay, integral inequalities, Wirtinger inequality, spatiotemporal chaos, image encryption, chaotic cryptosystem, key space

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