Skip to main content
SpringerLink
Log in

Cross-correlated sine-Wiener bounded noises-induced logical stochastic resonance

  • Published:
Pramana Aims and scope Submit manuscript

Abstract

Noise improves the reliability of logic operations if noise parameter is in certain proper region (reliable region), which is known as logical stochastic resonance (LSR). LSR attracts much attention due to its potential application in new-style logic devices. However, nothing is reported about the effect of cross-correlated sine-Wiener (CCSW) bounded noises on the reliability and agility of logic operations. Here we explicitly demonstrate that in certain proper parameter regions of amplitude and correlation time of CCSW noises, CCSW noises can induce LSR. In addition, cross-correlation intensity of CCSW noises can drastically influence the range of reliable region. By comparison, moderate cross-correlation intensity can drastically destroy the reliability of the logic system, and strongly shrink the optimal parameter ranges, depending on cross-correlation time and amplitude. Moreover, for given amplitudes and cross-correlation time, a little faster logic operation can be obtained with increasing cross-correlation intensity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. L Lu et al, Nonlinear Dyn. 95(2), 1673 (2019)

    Article  Google Scholar 

  2. M Ge et al, Nonlinear Dyn. 99(3), 2355 (2020)

    Article  Google Scholar 

  3. Y Yao et al, Complexity 2020, 6821591 (2020)

    Google Scholar 

  4. R Benzi, A Sutera and A Vulpiani, J. Phys. 14(11), L453 (1981)

    ADS  Google Scholar 

  5. Y Xu et al, Nonlinear Dyn. 95(4), 3237 (2019)

    Article  Google Scholar 

  6. Z He and C Yao, The effect of oxygen concentration on the coupled neurons: Rich spiking patterns and synchronization (Science China Technological Sciences, 2020)

  7. Y Xu et al, Appl. Math. Comput. 385, 125427 (2020)

    MathSciNet  Google Scholar 

  8. A Pikovsky and J Kurths, Phys. Rev. Lett. 78(5), 775 (1997)

    Article  ADS  MathSciNet  Google Scholar 

  9. K Murali et al, Phys. Rev. Lett. 102(10), 104 (2009)

    Article  Google Scholar 

  10. V Kohar and S Sinha, Phys. Lett. A 376(8), 957 (2012)

    Article  ADS  Google Scholar 

  11. K P Singh and S Sinha, Phys. Rev. E 83(4), 046219 (2011)

    Article  ADS  Google Scholar 

  12. S N R Kazmi et al, Nanoscale 9(10), 3449 (2017)

    Article  Google Scholar 

  13. P Pfeffer et al, Phys. Rev. Appl. 4(1), 014011 (2015)

    Article  ADS  Google Scholar 

  14. J Wu et al, Chaos 27(6), 063105 (2017)

    Article  ADS  MathSciNet  Google Scholar 

  15. N Wang and A Song, IEEE Trans. Neural Networks 27(12), 2736 (2016)

    Article  MathSciNet  Google Scholar 

  16. E H Hellen et al, PLOS ONE 8(10), e76032 (2013)

    Article  ADS  Google Scholar 

  17. S de Franciscis, G Caravagna and A d’Onofrio, Natural Computing 13(3), 297 (2014)

    Article  MathSciNet  Google Scholar 

  18. G Q Cai and C Wu, Prob. Eng. Mech. 19(3), 197 (2004)

    Article  MathSciNet  Google Scholar 

  19. D Li et al, Nonlinear Dyn. 70(3), 2237 (2012)

    Article  Google Scholar 

  20. R V Bobryk and A Chrzeszczyk, Physica A 358(2), 263 (2005)

    Article  ADS  Google Scholar 

  21. A Donofrio and A Gandolfi, Phys. Rev. E 82(6), 061901 (2010)

    Article  ADS  MathSciNet  Google Scholar 

  22. Y Yao, M Yi and D Hou, Int. J. Mod. Phys. B 31(28), 1750204 (2017)

    Article  ADS  Google Scholar 

  23. Y Yao et al, Physica A 492, 1247 (2018)

    Article  ADS  MathSciNet  Google Scholar 

  24. Y Yao and J Ma, Cognitive Neurodynamics 12(3), 343 (2018)

    Article  Google Scholar 

  25. Y Yao et al, Complexity 2018, 8793298 (2018)

    Google Scholar 

  26. G Cheng et al, Physica A 520, 361 (2019)

    Article  ADS  MathSciNet  Google Scholar 

  27. G Cheng et al, Chaos Solitons Fractals 131, 109514 (2020)

    Article  Google Scholar 

  28. L Zhang et al, Phys. Rev. E 96(5), 052203 (2017)

    Article  ADS  Google Scholar 

  29. M Aravind, K Murali and S Sinha, Phys. Lett. A 382(24), 1581 (2018)

  30. W Guo, L-C Du and D-C Mei, Physica A 391(4), 1270 (2012)

    Article  ADS  Google Scholar 

  31. Y Yao and J Ma, Int. J. Bifurc. Chaos 30(13), 2050196 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, Hubei, China

    Yuangen Yao

Authors
  1. Yuangen Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuangen Yao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yao, Y. Cross-correlated sine-Wiener bounded noises-induced logical stochastic resonance. Pramana - J Phys 95, 77 (2021). https://doi.org/10.1007/s12043-021-02120-1

Download citation

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12043-021-02120-1

Keywords

PACS Nos

Search

Navigation