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Evaluation of the Impact of State’s Administrative Efforts on Tax Potential Using Sugeno-Type Fuzzy Inference Method

  • Samir RustamovEmail author
  • Akif Musayev
  • Shahzada Madatova
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)

Abstract

Evaluation of the impact of state’s administrative efforts on tax potential via Sugeno-type fuzzy inference method has been investigated in the article. For this purpose, input data of the model has been fuzzified on the base of expert knowledge via different membership functions, and the output function has been evaluated on the base of the determined rules. Effective model-specific parameters have been selected in order to calculate the output function. The results obtained by Sugeno-type fuzzy inference method have been compared with the results evaluated via the Mamdani-type fuzzy inference method.

Keywords

Tax potential Sugeno-type fuzzy inference method Membership functions 

References

  1. 1.
    Tax reforms in EU Member States, Tax policy challenges for economic growth and fiscal sustainability. European Economy 008/2015Google Scholar
  2. 2.
    Jean-François, B., Maïmouna, D.: Tax potential and tax effort: an empirical estimation for non-resource tax revenue and VAT’s revenue. 2016.10Google Scholar
  3. 3.
    Slobodchikov, D.N.: Dissertation. Tax potential in the system of inter-budgetary relations (Code, HAC- 08.00.10) (2010)Google Scholar
  4. 4.
    Musayev, A.F.: Tax potential and its assessment methods. Tax magazine of Azerbaijan N5(119) (2014)Google Scholar
  5. 5.
    Musayev, A.F.: Innovation Economics and Tax Stimulation, p. 184. The University of Azerbaijan, Baku (2014)Google Scholar
  6. 6.
    Musayev, A., Gahramanov, A.: Introduction to Econometrics, p. 173. The University of Azerbaijan, Baku (2011)Google Scholar
  7. 7.
    Mathwork. Fuzzy Inference Process. http://www.mathworks.com/
  8. 8.
    Fausto, C.: A Takagi-Sugeno fuzzy inference system for developing a sustainability index of biomass. Sustainability 7 (2015)Google Scholar
  9. 9.
    Shleeg, A.A., Ellabib, I.M.: Comparison of Mamdani and Sugeno fuzzy interference systems for the breast cancer risk. Int. J. Comput., Electr. Autom. Control. Inf. Eng. 7(10), 387–391 (2013)Google Scholar
  10. 10.
    Kamyar, M.: Takagi-Sugeno fuzzy modeling for process control. Industrial Automation, Robotics and Artificial Intelligence (2008)Google Scholar
  11. 11.
    Sugeno, M.: Industrial Applications of Fuzzy Control. Elsevier, Amsterdam (1985)zbMATHGoogle Scholar
  12. 12.
    Yen, J., Langari, R.: Fuzzy Logic. Pearson Education, London (2004)Google Scholar
  13. 13.
    Ross, T.J.: Fuzzy Logic with Engineering Applications. Wiley, Hoboken (2010)CrossRefGoogle Scholar
  14. 14.
    Koop, G.: Analysis of Economic Data. Wiley, Chichester (2000)Google Scholar
  15. 15.
    Musayev, A., Madatova, S., Rustamov, S.: Evaluation of the impact of the tax legislation reforms on the tax potential by fuzzy inference method. Procedia Comput. Sci. 102, 507–514 (2016)CrossRefGoogle Scholar
  16. 16.
    Musayev, A., Madatova, S., Rustamov, S.: Mamdani-type fuzzy inference system for evaluation of tax potential. In: Recent Developments and the New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol. 361, pp. 511–523. Springer (2018)Google Scholar
  17. 17.
    Kamil, A., Rustamov, S., Clements, M.A., Mustafayev, E.: Adaptive neuro-fuzzy inference system for classification of texts. In: Recent Developments and the New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol. 361, pp. 63–70. Springer (2018)Google Scholar
  18. 18.
    Rustamov, S.: A hybrid system for subjectivity analysis. Adv. Fuzzy Syst. (2018)Google Scholar
  19. 19.
    Rustamov, S., Mustafayev, E., Clements, M.A.: Context analysis of customer requests using a hybrid adaptive neuro fuzzy inference system and hidden Markov models in the natural language call routing problem. Open Eng. 8(1), 61–68 (2018)CrossRefGoogle Scholar
  20. 20.
    Rustamov, S.S.: An application of neuro-fuzzy model for text and speech understanding systems. In: PCI 2012, The IV International Conference “Problems of Cybernetics and Informatics”, Baku, Azerbaijan, vol. I, pp. 213–217 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Samir Rustamov
    • 1
    Email author
  • Akif Musayev
    • 2
    • 3
  • Shahzada Madatova
    • 4
  1. 1.ADA University, Institute of Control Systems of ANASBakuAzerbaijan
  2. 2.Institute of EconomicsANASBakuAzerbaijan
  3. 3.Near East UniversityNicosiaTurkey
  4. 4.Azerbaijan State University of Economics (UNEC)BakuAzerbaijan

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