Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Fractional stochastic resonance multi-parameter adaptive optimization algorithm based on genetic algorithm

  • Smart Data Aggregation Inspired Paradigm & Approaches in IoT Applns
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The output effect of fractional-order stochastic resonance (FOSR) system is affected by many factors such as input system parameters and noise intensity. In practice, many tests are needed to adjust parameters to achieve the optimal effect, and this way of “trial and error” greatly limits the application prospect of FOSR. Based on genetic algorithm, a suitable adaptive function was established to adjust the multiple parameters, including the fractional order, system parameters, and the input noise intensity of the fractional bistable system. Simulation results showed that the algorithm can achieve joint optimization of these parameters. It was proved that this algorithm is conducive to the real-time adaptive adjustment of the FOSR system in practical applications and conducive to the application and extension of FOSR in weak signal detection and other fields. The proposed algorithm has certain practical value.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Benzi R, Parisi G, Sutera A (1982) Stochastic resonance in climatic change. Tellus 34(1):10–16

    Article  Google Scholar 

  2. Zhou T, Moss F (1990) Analog simulations of stochastic resonance. Phys Rev A 41(8):4255

    Article  Google Scholar 

  3. Gammaitoni L, Marchesoni F, Menichella-Saetta E (1989) Stochastic resonance in bistable systems. Phys Rev Lett 62(62):349–352

    Article  Google Scholar 

  4. Fauve S, Heslot F (1983) Stochastic resonance in a bistable system. Phys Lett A 97(1):5–7

    Article  Google Scholar 

  5. Kou SC, Xie XS (2004) Generalized Langevin equation with fractional Gaussian noise: subdiffusion within a single protein molecule. Phys Rev Lett 93(18):180603

    Article  Google Scholar 

  6. Weina Z, Lin M (2013) Weak signal intelligent detection system based on stochastic resonance and artificial fish swarm algorithm. Chin J Sci Instrum 34(11):2464–2470

    Google Scholar 

  7. Magin RL, Abdullah O, Baleanu D (2008) Anomalous diffusion expressed through fractional order differential operators in the Bloch–Torrey equation. J Magn Reson 190(2):255–270

    Article  Google Scholar 

  8. Kocaoglu M (2014) Stochastic resonance in graphene bilayer optical nanoreceivers. IEEE Trans Nanotechnol 13(6):1107–1117

    Article  Google Scholar 

  9. Herrmann S, Imkeller P, Pavlyukevich I (2014) Stochastic resonance: a mathematical approach in the small noise limit, vol 194. American Mathematical Society, Providence, p 189

    MATH  Google Scholar 

  10. Litak G, Borowiec M (2014) On simulation of a bistable system with fractional damping in the presence of stochastic coherence resonance. Nonlinear Dyn 77(77):681–686

    Article  MathSciNet  Google Scholar 

  11. Zheng R, Nakano K, Hu H (2014) An application of stochastic resonance for energy harvesting in a bistable vibrating system. J Sound Vib 333(12):2568–2587

    Article  Google Scholar 

  12. Sun Q, Liu H, Nan H (2015) Nonlinear restoration of pulse and high noisy images via stochastic resonance. Sci Rep 5(4):1–6

    Google Scholar 

  13. Bito J, Jeong S, Tentzeris MM (2016) A real-time electrically controlled active matching circuit utilizing genetic algorithms for wireless power transfer to biomedical implants. IEEE Trans Microw Theory 64(2):365–374

    Article  Google Scholar 

  14. Jung P, HaoNggi P (2017) Amplification of small signals via stochastic resonance. Phys Rev A 44(12):8032–8042

    Article  Google Scholar 

  15. Yang JH, Sanjuán MAF, Liu HG (2016) Stochastic P-bifurcation and stochastic resonance in a noisy bistable fractional-order system. Commun Nonlinear Sci Numer Simul 41:104–117

    Article  MathSciNet  Google Scholar 

  16. Liu L, Zheng L, Zhang X (2016) Fractional anomalous diffusion with Cattaneo–Christov flux effects in a comb-like structure. Appl Math Model 40(13–14):6663–6675

    Article  MathSciNet  Google Scholar 

  17. Wang W, Yan Z, Liu X (2017) The escape problem and stochastic resonance in a bistable system driven by fractional Gaussian noise. Phys Lett A 381(29):2324–2336

    Article  MathSciNet  Google Scholar 

  18. He P, Dai S (2014) Stealth real-time paths planning for heterogeneous UAV formation based on parallel niche genetic algorithm. J Comput Inf Syst 10(15):6731–6740

    Google Scholar 

  19. Coury DV, Silva RPM, Delbem ACB (2014) Programmable logic design of a compact Genetic Algorithm for phasor estimation in real-time. Electric Power Syst Res 107(2):109–118

    Article  Google Scholar 

Download references

Acknowledgements

This project is supported by National Natural Science Foundation of China (Grant No. 51775530) and National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No. 2015ZX02101).

Author information

Authors and Affiliations

  1. College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou, Zhejiang, China

    Yongjun Zheng, Ming Huang, Yi Lu & Wenjun Li

Authors
  1. Yongjun Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wenjun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongjun Zheng.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, Y., Huang, M., Lu, Y. et al. Fractional stochastic resonance multi-parameter adaptive optimization algorithm based on genetic algorithm. Neural Comput & Applic 32, 16807–16818 (2020). https://doi.org/10.1007/s00521-018-3910-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3910-6

Keywords

Search

Navigation