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A Forecast Model for Language Under the Influence of Immigration

Received: 18 July 2018     Published: 19 July 2018
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Abstract

Under the influence of globalization, population distribution of different languages has become a matter of universal concern. In this paper, the mater is considered from three aspects. First of all, Gray Model is used to predict the number of second language speakers. Then, the influencing factors are taken into account to predict the number of net immigrants through the BP neural network. Finally, while considering the impact of immigration, Logistic growth model are used to predict the population of native speakers. In order to analyze the demographic changes in different regions, firstly, logistic growth model is used to predict the population in different regions. Secondly, influencing factors are combined with the BP neural network to predict the number of net immigrants in the next 50 years. Based on the forecast, French will surpass the Punjabi, which ranks number nine to become the tenth language with the greatest number of native speakers. As for the total number of speakers using a particular language, native speakers plus second language speakers are considered. The Gray Model is used to predict the number of second language speakers in different regions over the next 50 years. Based on the forecast, no language in the current top-ten lists will be replaced by another language. Net immigration is used to reflect the changing global language distribution over the next 50 years. GDP per capita, education funds, medical expenses and tourism revenue are identified as four factors. BP neural network are used to find out the relationship between influencing factors and net immigrants, and then the number of net immigration of different regions in the next 50 years are predicted. Numerical results demonstrated that model in this paper is efficient and promising.

Published in Applied and Computational Mathematics (Volume 7, Issue 3)
DOI 10.11648/j.acm.20180703.17
Page(s) 121-129
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

The Distribution of Languages, The Number of Speakers, Gray Model, BP Natural Network, Logistic Growth Model

References
[1] Brownell M D, Ekuma O, Nickel N C, et al. A population-based analysis of factors that predict early language and cognitive development [J]. Early Childhood Research Quarterly, 2016, 35:6-18.
[2] Molinaro N, Giannelli F, Caffarra S, et al. Hierarchical levels of representation in language prediction: The influence of first language acquisition in highly proficient bilinguals. [J]. Cognition, 2017, 164:61.
[3] Wu L F, Liu S F, Liu J. GM (1, 1) model based on fractional order accumulating method and its Stability [J]. Control & Decision, 2014, 29 (5):919-924.
[4] Qi D. On Design of the BP Neural Network [J]. Computer Engineering & Design, 1998.
[5] Zhang G. Research and simulation of population forecasting model of BP Neural Network [J]. Intelligent Computer & Applications, 2016.
[6] Law R, Murrell D J, Dieckmann U. Erratum: Population Growth in Space and Time: Spatial Logistic Equations [J]. Ecology, 2003, 84 (2):535-535.
[7] Kozlov V, Radosavljevic S, Wennergren U. Large time behavior of the logistic age-structured population model in a changing environment [J]. Asymptotic Analysis, 2017, 102(1-2):21-54.
[8] Zhang X, Liu Y. The city taxi quantity prediction via GM-BP model [C]// IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems. IEEE, 2015:1594-1598.
[9] Niyogi P, Berwick R C. The proper treatment of language acquisition and change in a population setting. [J]. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106 (25):10124-10129.
[10] Ortega F, Peri G. The effect of income and immigration policies on international migration [M]// The Economics of International Migration. 2016:333-360.
[11] Hening A, Nguyen D H, Yin G. Stochastic population growth in spatially heterogeneous environments: the density-dependent case [J]. Journal of Mathematical Biology, 2017, 76 (3):1-58.
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  • APA Style

    Geng Liu, Keai Yang, Zhuojun Yao. (2018). A Forecast Model for Language Under the Influence of Immigration. Applied and Computational Mathematics, 7(3), 121-129. https://doi.org/10.11648/j.acm.20180703.17

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    ACS Style

    Geng Liu; Keai Yang; Zhuojun Yao. A Forecast Model for Language Under the Influence of Immigration. Appl. Comput. Math. 2018, 7(3), 121-129. doi: 10.11648/j.acm.20180703.17

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    AMA Style

    Geng Liu, Keai Yang, Zhuojun Yao. A Forecast Model for Language Under the Influence of Immigration. Appl Comput Math. 2018;7(3):121-129. doi: 10.11648/j.acm.20180703.17

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  • @article{10.11648/j.acm.20180703.17,
      author = {Geng Liu and Keai Yang and Zhuojun Yao},
      title = {A Forecast Model for Language Under the Influence of Immigration},
      journal = {Applied and Computational Mathematics},
      volume = {7},
      number = {3},
      pages = {121-129},
      doi = {10.11648/j.acm.20180703.17},
      url = {https://doi.org/10.11648/j.acm.20180703.17},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20180703.17},
      abstract = {Under the influence of globalization, population distribution of different languages has become a matter of universal concern. In this paper, the mater is considered from three aspects. First of all, Gray Model is used to predict the number of second language speakers. Then, the influencing factors are taken into account to predict the number of net immigrants through the BP neural network. Finally, while considering the impact of immigration, Logistic growth model are used to predict the population of native speakers. In order to analyze the demographic changes in different regions, firstly, logistic growth model is used to predict the population in different regions. Secondly, influencing factors are combined with the BP neural network to predict the number of net immigrants in the next 50 years. Based on the forecast, French will surpass the Punjabi, which ranks number nine to become the tenth language with the greatest number of native speakers. As for the total number of speakers using a particular language, native speakers plus second language speakers are considered. The Gray Model is used to predict the number of second language speakers in different regions over the next 50 years. Based on the forecast, no language in the current top-ten lists will be replaced by another language. Net immigration is used to reflect the changing global language distribution over the next 50 years. GDP per capita, education funds, medical expenses and tourism revenue are identified as four factors. BP neural network are used to find out the relationship between influencing factors and net immigrants, and then the number of net immigration of different regions in the next 50 years are predicted. Numerical results demonstrated that model in this paper is efficient and promising.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - A Forecast Model for Language Under the Influence of Immigration
    AU  - Geng Liu
    AU  - Keai Yang
    AU  - Zhuojun Yao
    Y1  - 2018/07/19
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    DO  - 10.11648/j.acm.20180703.17
    T2  - Applied and Computational Mathematics
    JF  - Applied and Computational Mathematics
    JO  - Applied and Computational Mathematics
    SP  - 121
    EP  - 129
    PB  - Science Publishing Group
    SN  - 2328-5613
    UR  - https://doi.org/10.11648/j.acm.20180703.17
    AB  - Under the influence of globalization, population distribution of different languages has become a matter of universal concern. In this paper, the mater is considered from three aspects. First of all, Gray Model is used to predict the number of second language speakers. Then, the influencing factors are taken into account to predict the number of net immigrants through the BP neural network. Finally, while considering the impact of immigration, Logistic growth model are used to predict the population of native speakers. In order to analyze the demographic changes in different regions, firstly, logistic growth model is used to predict the population in different regions. Secondly, influencing factors are combined with the BP neural network to predict the number of net immigrants in the next 50 years. Based on the forecast, French will surpass the Punjabi, which ranks number nine to become the tenth language with the greatest number of native speakers. As for the total number of speakers using a particular language, native speakers plus second language speakers are considered. The Gray Model is used to predict the number of second language speakers in different regions over the next 50 years. Based on the forecast, no language in the current top-ten lists will be replaced by another language. Net immigration is used to reflect the changing global language distribution over the next 50 years. GDP per capita, education funds, medical expenses and tourism revenue are identified as four factors. BP neural network are used to find out the relationship between influencing factors and net immigrants, and then the number of net immigration of different regions in the next 50 years are predicted. Numerical results demonstrated that model in this paper is efficient and promising.
    VL  - 7
    IS  - 3
    ER  - 

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Author Information
  • Department of Mathematics, Rongcheng College of Harbin University of Science and Technology, Weihai, China

  • Department of Economics & Management, Rongcheng College of Harbin University of Science and Technology, Weihai, China

  • Department of Economics & Management, Rongcheng College of Harbin University of Science and Technology, Weihai, China

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