Skip to main content

Prediction of the Network Administration Course Results Based on Fuzzy Inference

  • 605 Accesses

Part of the Topics in Intelligent Engineering and Informatics book series (TIEI,volume 7)

Abstract

The prediction of the number of students who will pass or fail the exams in the case of a subject can be very useful information for resource allocation planning purposes. In this chapter, we report on the development of a fuzzy model, that based on the previous performance of currently enrolled students, gives a prediction for the number of students who will fail the exams of the Network Administration course at the end of the autumn semester. These students will usually re-enroll for the course in the spring semester and, conforming to previous experience, will constitute the major part of the enrolling students. The fuzzy model uses a low number of rules and applies a fuzzy rule interpolation based technique (Least Squares based Fuzzy Rule Interpolation) for inference.

Keywords

  • Administration Courses
  • Network Administration (NA)
  • Fuzzy Rule Interpolation (FRI)
  • Autumn Semester
  • Initial Fuzzy System

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-01919-2_2
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-01919-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   139.99
Price excludes VAT (USA)
Hardcover Book
USD   139.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Baranyi, P., Kóczy, L.T., Gedeon, T.D.: A generalized concept for fuzzy rule interpolation. IEEE Trans. Fuzzy Syst. 12(6), 820–837 (2004)

    Google Scholar 

  2. Blažič, S., Škrjanc, I., Matko, D.: Globally stable direct fuzzy model reference adaptive control. Fuzzy Sets Syst. 139(1), 3–33 (2003)

    CrossRef  MATH  Google Scholar 

  3. Botzheim, J., Hámori, B., Kóczy, L.T.: Extracting trapezoidal membership functions of a fuzzy rule system by bacterial algorithm. In: 7th Fuzzy Days, pp. 218–227. Springer, Dortmund (2001)

    Google Scholar 

  4. Brownlee, J.: Clonal selection algorithms. CIS Technical Report 070209A. Swinburne University of Technology, Melbourne, Australia (2007)

    Google Scholar 

  5. de Castro, L.N., von Zuben, F.J.: Artificial Immune Systems—Part I: Basic Theory and Applications. Department of Computer Engineering and Industrial Automation, School of Electrical and Computer Engineering, State University of Campinas, Brazil, TR DCA 01/99 (1999)

    Google Scholar 

  6. Chen, S.M., Ko, Y.K.: Fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on \(\alpha \) -cuts and transformations techniques. IEEE Trans. Fuzzy Syst. 16(6), 1626–1648 (2008)

    CrossRef  Google Scholar 

  7. Detyniecki, M., Marsala, C., Rifqi, M.: Double-linear fuzzy interpolation method. In: IEEE International Conference on Fuzzy Systems (FUZZ: 2011), Taipei, pp. 455–462, 27–30 June 2011

    Google Scholar 

  8. Devasenapati, S.B., Ramachandran, K.I.: Hybrid fuzzy model based expert system for misfire detection in automobile engines. Int. J. Artif. Intell. 7(A11), 47–62 (2011)

    Google Scholar 

  9. FRI Matlab ToolBox. http://fri.gamf.hu

  10. Gál, L., Botzheim, J., Kóczy, L.T.: Advanced bacterial memetic algorithms. In: 1st Győr Symposium Computational Intelligence, pp. 57–60, (2008)

    Google Scholar 

  11. Huang, Z.H., Shen, Q.: Fuzzy interpolation with generalized representative values. In. Proceedings of the UK Workshop on Computational Intelligence, pp. 161–171 (2004)

    Google Scholar 

  12. Johanyák, Z.C.: Fuzzy rule interpolation based on subsethood values. In: Proceedings of 2010 IEEE International Conference on Systems Man, and Cybernetics (SMC 2010), pp. 2387–2393, 10–13 Oct 2010

    Google Scholar 

  13. Johanyák, Z.C.: Clonal selection based parameter optimization for sparse fuzzysys-tems. In. Proceedings of IEEE 16th International Conference on Intelligent Engineering Systems (INES: 2012), pp. 369–373, Lisbon, 13–15 June 2012

    Google Scholar 

  14. Johanyák, Z.C., Kovács, S.: Fuzzy rule interpolation by the least squares method. In: 7th International Symposium of Hungarian Researchers on Computational Intelligence (HUCI 2006), pp. 495–506, Budapest, ISBN 963 7154 54 X (2006)

    Google Scholar 

  15. Kovács, L.: Rule approximation in metric spaces. In: Proceedings of 8th IEEE International Symposium on Applied Machine Intelligence and Informatics SAMI 2010, pp. 49–52, Herl’any, Slovakia (2010)

    Google Scholar 

  16. Kovács, S.: Extending the fuzzy rule interpolation "FIVE" by fuzzy observation. advances in soft computing. In: Reusch, B. (ed.) Computational Intelligence, Theory and Applications, pp. 485–497. Springer, Germany (2006)

    Google Scholar 

  17. Perfilieva, I., Wrublova, M., Hodakova, P.: Fuzzy interpolation according to fuzzy and classical conditions. Acta Polytech. Hung. 7(4), 39–55 (2010)

    Google Scholar 

  18. Precup, R.-E., Preitl, S., Faur, G.: PI predictive fuzzy controllers for electrical drive speed control: methods and software for stable development. Comput. Ind. 52(3), 253–270 (2003)

    CrossRef  Google Scholar 

  19. Precup, R.-E., Preitl, S.E., Petriu, M., Tar, J.K., Tomescu, M.L., Pozna, C.: Generic two-degree-of-freedom linear and fuzzy controllers for integral processes. J. Franklin Inst. 346(10), 980–1003 (2009)

    MathSciNet  CrossRef  MATH  Google Scholar 

  20. Portik, T., Pokorádi, L.: Possibility of use of fuzzy logic in management. In: 16th Building Services. Mechanical and Building Industry days International Conference, pp. 353–360, Debrecen, Hungary, 14–15 Oct 2010

    Google Scholar 

  21. Shepard, D.: A two dimensional interpolation function for irregularly spaced data. In: Proceedings of the 23rd Annual International ACM SIGIR Conference, pp. 517–524 (1968)

    Google Scholar 

  22. Sinčák, P., Hric, M., Vaščák, J.: Neural networks classifiers based on membership function ARTMAP. In: Systematic Organisation of Information in Fuzzy System, Series: NATO Science Series. Subseries III: Computer and Systems Sciences, vol. 184, pp.321–333. IOS Press, Amsterdam (2003)

    Google Scholar 

  23. Tikk, D., Kóczy, L.T., Gedeon, T.D.: A survey on the universal approximation and its limits in soft computing techniques. Int. J. Approximate Reasoning 33, 185–202 (2003)

    CrossRef  MATH  Google Scholar 

  24. Vass, Gy., Kalmár, L. Kóczy, L.T.: Extension of the fuzzy rule interpolation method. In: International Conference on Fuzzy Sets Theory and Applications, Liptovsky M. Czechoslovakia, pp. 1–6 (1992)

    Google Scholar 

  25. Vincze, D., Kovács, S.: Incremental rule base creation with fuzzy rule interpolation-based Q-learning. Stud Comput Intell Comput Intell Eng 313, 191–203 (2010)

    CrossRef  Google Scholar 

Download references

Acknowledgments

This research was supported by the National Scientific Research Fund Grant OKTA K77809. The described work was carried out as part of the TÁMOP-4.2.2/B-10/1-2010-0008 project in the framework of the New Hungarian Development Plan. The realization of this project is supported by the European Union, co-financed by the European Social Fund.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Szilveszter Kovács .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Johanyák, Z.C., Kovács, S. (2014). Prediction of the Network Administration Course Results Based on Fuzzy Inference. In: Bognár, G., Tóth, T. (eds) Applied Information Science, Engineering and Technology. Topics in Intelligent Engineering and Informatics, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-01919-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-01919-2_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01918-5

  • Online ISBN: 978-3-319-01919-2

  • eBook Packages: EngineeringEngineering (R0)