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Main Factor Selection Algorithm and Stability Analysis of Regional FDI Statistics

Juan Huang1, Bifang Zhou1, Huajun Huang2,*, Dingwen Qing1, Neal N. Xiong3

1 Centre for Innovation Research in Social Governance, Changsha University of Science and Technology, Changsha, 410114, China
2 College of Information Technology and Management, Hunan University of Finance and Economics, Changsha, 410205, China
3 Department of mathematics and computer science, Northeastern State University, OK, 74464, USA

* Corresponding Author: Huajun Huang. Email: email

Intelligent Automation & Soft Computing 2021, 30(1), 303-318.


There are various influencing factors in regional FDI (foreign direct investment) and it is difficult to identify the main influencing factors. For this reason, a main factor selection algorithm is proposed in this article for the main factors affecting regional FDI statistics by analyzing the regional economic characteristics and the possible influencing factors in the regional FDI. Then, an example is used to illustrate its effectiveness and its stability. Firstly, the characteristics of regional economy and the regional FDI data are introduced to develop the main factor selection algorithm based on the adaptive Lasso problem for the regional FDI and to establish the corresponding computing procedure. Then, based on the regional FDI statistical data of six provinces in the central China, the main factor selection algorithm is used to filter out the insignificant factors and identify the main influencing factors for the different regional FDI statistics, including the mean values, the median values, the maximum values, and the minimum values. Finally, the proposed algorithm is validated through an accuracy test experiment performed in central China. On this basis, its corresponding stability with the noise error case is analyzed and the control stability range of the algorithm is determined.


Cite This Article

J. Huang, B. Zhou, H. Huang, D. Qing and N. N. Xiong, "Main factor selection algorithm and stability analysis of regional fdi statistics," Intelligent Automation & Soft Computing, vol. 30, no.1, pp. 303–318, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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