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RETRACTED ARTICLE: The application of particle swarm optimization for the training of neural network in English teaching

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This article was retracted on 08 December 2022

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Abstract

Particle swarm optimization and neural network algorithm are very novel computer intelligent algorithms, and with the development of computer technology, these algorithms have been applied to various fields. Because of obvious advantages, in this paper, the particle swarm optimization and neural network algorithms were applied to English teaching. English is an international language, and the teaching of English is the basis of learning English. Therefore, the study of English teaching can promote the process of internationalization, which is more convenient to spread the knowledge of different countries, and it also makes the economic trades between different countries go on faster. Therefore, the use of particle swarm optimization in the training of the neural network and its application in English teaching are subjects that are worthy of study. In this paper, the current research status at home and abroad was firstly analyzed, and the shortcomings of the traditional algorithms were improved; then, the improved algorithm was applied to the study of English teaching; finally, the effectiveness of the algorithm was verified by the experiment simulation.

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References

  1. Rao, P.C.S., Jana, P.K., Banka, H.: A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wirel. Netw. 23(7), 2005–2020 (2017)

    Article  Google Scholar 

  2. Ambursa, F.U., Latip, R., Abdullah, A., et al.: A particle swarm optimization and min–max-based workflow scheduling algorithm with QoS satisfaction for service-oriented grids. J. Supercomput. 73(5), 1–34 (2017)

    Article  Google Scholar 

  3. Jin, H.L., Kim, J.W., Song, J.Y., et al.: A novel memetic algorithm using modified particle swarm optimization and mesh adaptive direct search for PMSM design. IEEE Trans. Magn. 52(3), 1–4 (2017)

    Google Scholar 

  4. Collotta, M., Pau, G., Maniscalco, V.: A fuzzy logic approach by using particle swarm optimization for effective energy management in IWSNs. IEEE Trans. Ind. Electron. PP(99), 1 (2017)

    Google Scholar 

  5. Wu, S.L., Liu, Y.T., Hsieh, T.Y., et al.: Fuzzy integral with particle swarm optimization for a motor-imagery-based brain–computer interface. IEEE Trans. Fuzzy Syst. 25(1), 21–28 (2017)

    Article  Google Scholar 

  6. Han, H., Wu, X., Zhang, L., et al.: Self-organizing RBF neural network using an adaptive gradient multiobjective particle swarm optimization. IEEE Trans. Cybern. PP(99), 1–14 (2017)

    Google Scholar 

  7. Behnamian, J.: Heterogeneous networked cooperative scheduling with anarchic particle swarm optimization. IEEE Trans. Eng. Manag. PP(99), 1–13 (2017)

    Google Scholar 

  8. Abualigah, L.M., Khader, A.T.: Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J. Supercomput. 1, 1–23 (2017)

    Google Scholar 

  9. Poole, A., Kotsialos, A.: METANET validation of the large-scale manchester ring-road network using gradient-based and particle swarm optimization. IEEE Trans. Intell. Transp. Syst. PP(99), 1–11 (2017)

    Google Scholar 

  10. Baghaee, H.R., Mirsalim, M., Gharehpetian, G.B., et al.: A hybrid ANFIS/ABC-based online selective harmonic elimination switching pattern for cascaded multi-level inverters of microgrids. IEEE Trans. Ind. Electron. PP(99), 1 (2017)

    Google Scholar 

  11. Zhu, Q., Lin, Q., Chen, W., et al.: An external archive-guided multiobjective particle swarm optimization algorithm. IEEE Trans. Cybern. 47(9), 2794 (2017)

    Article  Google Scholar 

  12. Jordehi, A.R.: Enhanced leader particle swarm optimisation (ELparticle swarm optimization): an efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules. Sol. Energy 159, 78–87 (2018)

    Article  Google Scholar 

  13. Tang, B., Zhu, Z., Shin, H.S., et al.: A framework for multi-objective optimisation based on a new self-adaptive particle swarm optimisation algorithm. Inf. Sci. 420, 364–385 (2017)

    Article  Google Scholar 

  14. Jin, H.L., Song, J.Y., Kim, D.W., et al.: Particle swarm optimization algorithm with intelligent particle number control for optimal design of electric machines. IEEE Trans. Ind. Electron. PP(99), 1 (2017)

    Google Scholar 

  15. Ismail, A.M., Mohamad, M.S., Majid, H.A., et al.: An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways. Biosystems 162, 81–89 (2017)

    Article  Google Scholar 

  16. Saxena, N., Mishra, K.K.: Improved multi-objective particle swarm optimization algorithm for optimizing watermark strength in color image watermarking. Appl. Intell. 4, 1–20 (2017)

    Google Scholar 

  17. Lin, T.Y., Yeh, J.T., Kuo, W.S.: Using particle swarm optimization algorithm to search for a power ascension path of boiling water reactors. Ann. Nucl. Energy 102, 37–46 (2017)

    Article  Google Scholar 

  18. Abdeyazdan, M.: A new method for the informed discovery of resources in the grid system using particle swarm optimization algorithm (RDT_particle swarm optimization). J. Supercomput. 2, 1–24 (2017)

    Google Scholar 

  19. Saleh, A.A., Adail, A.S., Wadoud, A.A.: Optimal phasor measurement units placement for full observability of power system using improved particle swarm optimisation. IET Gener. Transm. Distrib. 11(7), 1794–1800 (2017)

    Article  Google Scholar 

  20. Sun, W., Lin, A., Yu, H., et al.: All-dimension neighborhood based particle swarm optimization with randomly selected neighbors. Inf. Sci. 405, 141–156 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of International Studies, University of Science and Technology Liaoning, Anshan, Liaoning, China

    Xiaoli Huang

  2. Liaoning Zhuoyi Technology Co., Ltd., Yingkou, Liaoning, China

    Fanlei Kong

Authors
  1. Xiaoli Huang
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  2. Fanlei Kong
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Correspondence to Xiaoli Huang.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10586-022-03854-2

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Huang, X., Kong, F. RETRACTED ARTICLE: The application of particle swarm optimization for the training of neural network in English teaching. Cluster Comput 22 (Suppl 2), 3989–3998 (2019). https://doi.org/10.1007/s10586-018-2590-4

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  • DOI: https://doi.org/10.1007/s10586-018-2590-4

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