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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Baranyi, P., Kóczy, L.T., Gedeon, T.D.: A generalized concept for fuzzy rule interpolation. IEEE Trans. Fuzzy Syst. 12(6), 820–837 (2004)
Blažič, S., Škrjanc, I., Matko, D.: Globally stable direct fuzzy model reference adaptive control. Fuzzy Sets Syst. 139(1), 3–33 (2003)
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)
Brownlee, J.: Clonal selection algorithms. CIS Technical Report 070209A. Swinburne University of Technology, Melbourne, Australia (2007)
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)
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)
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
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)
FRI Matlab ToolBox. http://fri.gamf.hu
Gál, L., Botzheim, J., Kóczy, L.T.: Advanced bacterial memetic algorithms. In: 1st Győr Symposium Computational Intelligence, pp. 57–60, (2008)
Huang, Z.H., Shen, Q.: Fuzzy interpolation with generalized representative values. In. Proceedings of the UK Workshop on Computational Intelligence, pp. 161–171 (2004)
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
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
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)
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)
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)
Perfilieva, I., Wrublova, M., Hodakova, P.: Fuzzy interpolation according to fuzzy and classical conditions. Acta Polytech. Hung. 7(4), 39–55 (2010)
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)
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)
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
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)
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)
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)
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)
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)
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
Corresponding author
Editor information
Editors and Affiliations
Rights 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)