The Research of Tax Inspection Based on Generalized Regression Neural Network

DOI: 10.4236/oalib.1100949   PDF   HTML   XML   752 Downloads   1,074 Views  


This paper tries to use the generalized regression neural network (in short: GRNN) to assist tax inspection case selection. First, this paper briefly introduces the theory of generalized regression neural network and applies it in the tax inspection. Second, it analyzes the financial statements and tax returns of 93 commercial enterprises, and then establishes the GRNN model and gets the analyzing result. Finally, the result is compared with the known taxation case. Then we get the conclusion that the generalized regression neural network method can help the tax inspection case selection and improve the efficiency and effectiveness of inspection work.

Share and Cite:

Chen, S. and Liu, X. (2015) The Research of Tax Inspection Based on Generalized Regression Neural Network. Open Access Library Journal, 2, 1-5. doi: 10.4236/oalib.1100949.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] He, Z. (2007) On the Reform Tendency of Tax Inspection System of China in the New Situation. Southwestern University of Finance and Economics, Chengdu.
[2] Specht, D.F.A. (1991) General Regression Neural Network. IEEE Transaction on Neural Networks, 2, 568-576.
[3] (2010) 30 Case Study of MATLAB Neural Network. Beihang University Press, Beijing.
[4] Chen, Y. (2004) Research on Sampling of Tax-Checking. Tianjin University, Tianjin.
[5] Guan, X. (2005) Data Mining Research on Sampling of Tax-Checking. Liaoning Technical University, Fuxin.
[6] Scott, D.W. (1992) Multivariate Density Estimation: Theory, Practice and Visualization. Wiley, New York, 45-67.
[7] Chen, S.H. and Zhang, Y.M. (2009) Application of Binary Logistic Regression Analysis in Tax Inspection. Financial Computer of Huanan, 6, 48-49.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.