Skip to main content

Advertisement

Springer Nature Link
Log in

Multi-parameter online optimization algorithm of BP neural network algorithm in Internet of Things service

  • S.I.: DPTA Conference 2019
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

With the development of science and technology, the application of the Internet of Things (IOT) is becoming more and more widespread. Applying BP neural network algorithms (NNA) to the IOT system will help improve the performance of the IOT system. The research purpose of this paper is to solve the problems of long-parameter measurement cycle and untimely feedback of the existing IOT online measurement system. In this paper, a multi-parameter (MP) IOT online measurement system based on BP NNA is designed, and a simulation test experiment is performed. The MP online measurement IOT system based on the BP NNA completes the parameter collection, analysis, and display through the perception layer, network transmission layer, and application layer. The core is that the system application layer adds the BP NNA to optimize real-time acquisition parameters, processing to reduce parameter measurement time. It can be known from algorithm simulation experiments that the online measurement system based on the BP NNA proposed in this paper uses the BP NNA to predict the absolute error value of the final moisture content and the measured moisture content within 0.3, and the absolute error of the moisture content value in actual production. It is acceptable in the range of 0.5, which speeds up data collection time. This system has a very good effect on improving the feedback adjustment speed of the manufacturing process system.

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Li S, Da Li X, Zhao S (2015) The Internet of Things: a survey. Inf Syst Front 17(2):243–259

    Article  Google Scholar 

  2. Perera C, Liu CH, Jayawardena S (2015) The emerging Internet of Things Marketplace from an industrial perspective: a survey. IEEE Trans Emerg Top Comput 3(4):585–598

    Article  Google Scholar 

  3. Razzaque MA, Milojevic-Jevric M, Palade A (2017) Middleware for Internet of Things: a survey. IEEE Internet Things J 3(1):70–95

    Article  Google Scholar 

  4. Palade A, Cabrera C, Li F (2018) Middleware for internet of things: an evaluation in a small-scale IOT environment. J Reliab Intell Environ 4:1–21

    Article  Google Scholar 

  5. Zhang C, Wang X, Li F, Huang M (2019) NNIRSS: neural network-based intelligent routing scheme for SDN. Neural Comput Appl 31(10):6189–6205

    Article  Google Scholar 

  6. Song D-M, Shi H-T, Shan X-J (2015) A tentative test on moderately strong earthquake prediction in china based on thermal anomaly information and bp neural network. Seismol Geol 37(2):649–660

    Google Scholar 

  7. Li S, Tryfonas T, Li H (2016) The Internet of Things: a security point of view. Internet Res 26(2):337–359

    Article  Google Scholar 

  8. Laghari S, Niazi MA (2016) Modeling the Internet of Things, self-organizing and other complex adaptive communication networks: a cognitive agent-based computing approach. PLoS ONE 11(1):e0146760

    Article  Google Scholar 

  9. Wenchang Z, Lianqing Z, Xiaoping L (2015) Multi-parameter extraction of flow cytometer based on grey prediction recovery algorithm. Chin Jo Sci Instrum 36(7):1660–1665

    Google Scholar 

  10. Gan L, Wang R, Liu D (2016) Spatial-division multiplexed Mach–Zehnder interferometers in heterogeneous multicore fiber for multi-parameter measurement. IEEE Photon J 8(1):1–1

    Article  Google Scholar 

  11. Qiu L, Xiao Y, Zhao W (2016) Laser confocal interference multi-parameter measurement method for spherical lens. IEEE Photon Technol Lett PP(99):1

    Google Scholar 

  12. Liang Z, Niu S, Jin N (2017) Current status and development trend of AC transmission line parameter measurement. Autom Electr Power Syst 41(11):181–191

    Google Scholar 

  13. Yang L, Li W, Ghandehari M (2017) People-centric cognitive Internet of Things for the quantitative analysis of environmental exposure. IEEE Internet Things J PP(99):1

    Google Scholar 

  14. Sicari A, Rizzardi A, Grieco LA (2015) Security, privacy and trust in Internet of Things: the road ahead. Comput Netw 76(C):146–164

    Article  Google Scholar 

  15. Tan X, Ji Z, Zhang Y (2018) Non-invasive continuous blood pressure measurement based on mean impact value method, BP neural network, and genetic algorithm. Technol Health Care Off J Eur Soc Eng Med 26(6):1–15

    Google Scholar 

  16. Aydogan EK, Özmen M, Delice Y (2019) CBR-PSO: cost-based rough particle swarm optimization approach for high-dimensional imbalanced problems. Neural Comput Appl 31(10):6345–6363

    Article  Google Scholar 

  17. Bing W, Han S, Xiao J (2016) Error compensation based on BP neural network for airborne laser ranging. Opt Int J Light Electron Opt 127(8):4083–4088

    Article  Google Scholar 

  18. Wang S, Chen D, Hou P (2015) Determination of the sodium methylparaben content based on spectrum fluorescence spectral technology and GA-BP neural network. Guang Pu Xue Yu Guang Pu Fen XI 35(6):1606–1610

    Google Scholar 

  19. Yiming LIU, Zhuowei HU, Wenji ZHAO (2015) Research on spatial characteristics of regional poverty based on BP neural network: a case study of Wuling mountain area. J Geo-Inf Sci 17(1):69–77

    Google Scholar 

  20. Wang B, Xiaodu G, Ma L (2017) Temperature error correction based on BP neural network in meteorological wireless sensor network. Int J Sens Netw 23(4):265

    Article  Google Scholar 

  21. Liu Q, Yuan W, Wang B (2015) Concentrate grade prediction of gold ore based on GA-BP neural network. J Northeast Univ 36(2):237–240

    Google Scholar 

Download references

Acknowledgements

This work was supported by Humanities and Social Sciences Fund of the Ministry of Education(China) (Project Number: 19YJC630108; Project name: Research on the formation mechanism, evaluation and control mechanism of Internet supply chain financial operation risk: Based on Symbiosis Theory); Scientific Studies Program of Higher Education of Inner Mongolia Municipality (No. NJZY18181).

Author information

Authors and Affiliations

  1. School of Computer and Information Engineering, Inner Mongolia University of Finance and Economics, Hohhot Minzu College, Hohhot, 010070, China

    Pingquan Wang

  2. Hubei University of Economics, Wuhan, 430205, Hubei, China

    Xun Liu

  3. Chifeng University, Chifeng, 024005, China

    Zheng Han

Authors
  1. Pingquan Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xun Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Zheng Han
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xun Liu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All authors have seen the manuscript and approved to submit to your journal. We confirm that the content of the manuscript has not been published or submitted for publication elsewhere.

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

Wang, P., Liu, X. & Han, Z. Multi-parameter online optimization algorithm of BP neural network algorithm in Internet of Things service. Neural Comput & Applic 33, 505–515 (2021). https://doi.org/10.1007/s00521-020-04913-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04913-8

Keywords