• Peter Grabusts Rezekne Academy of Technologies (LV)




data analysis, fuzzy logi, , Matlab, modelling, teaching


There is a rapidly growing interest in Artificial Intelligence applications in various modern areas. Students are very interested in modern data mining methods such as artificial neural networks, fuzzy logic and clustering. Teaching experience in study work shows that students perceive graphical information better than analytical relationships during learning process. Many training courses operate with models that were previously only available in mathematics disciplines. The solution would be to use the Matlab package to implement different models in Artificial Intelligence areas. Often, an analytical solution or simulation model is much simpler than a visual Matlab model, but it provides an insight into the usefulness of using such models for prospective training purposes. In previous articles, the author has provided examples of how Matlab's capabilities can be used in economic studies, artificial neural networks, and clustering. Fuzzy logic methods are often undeservedly forgotten, although the implementation of their algorithms is relatively simple and can be implemented even for students. In the research part of the study the modelling capabilities in data mining studies are demonstrated with fuzzy logic algorithms and real examples.



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Dubois, D., & Prade, H. (1980). Fuzzy sets and systems: theory and applications. Academic Press, New York.

Esfandiari, R.S. (2013). Numerical methods for engineers and scientists using MATLAB. Chapman & Hall/CRC.

Grabusts, P. (2019). The possibilities of clustering learning methods in student education. Proceedings of the International scientific Conference „Society. Integration. Education”, Rezekne,May,24-25, Vol. 5., 344-354. DOI:


Fuzzy Example. (2019). Retrieved from https://es.mathworks.com/help/fuzzy/building-systems-with-fuzzy-logic-toolbox-software.html#brzqs45

Fuzzy Logic Toolbox. (2019). Retrieved from https://www.mathworks.com/products/fuzzy-logic.html

Kay, C. (1984). Mathematics for computer programmers. New Jersey: Prentice Hall.

Karel, P., & Tomas, Z. (2015). Multimedia teaching aid for students of basics of control theory in Matlab and Simulink. Procedia Engineering, Volume 100, 150–158. https://doi.org/10.1016/j.proeng.2015.01.353

Karris, S.T. (2006). Introduction to Simulink ® with engineering applications. Orchard Publications.

Kiusalaas, J. (2016). Numerical methods in engineering with MATLAB. 3e. Cambridge University Press.

Mamdani, E.H. (1977). Applications of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transactions on Computers, Vol. 26, No. 12, 1182-1191. DOI: https://doi.org/10.1109/TC.1977.1674779

Smith, D. (2013). Engineering computation with MATLAB. 3e. Pearson Education Inc.

Xue, D., & Chen, Y. (2013). System simulation techniques with MATLAB and Simulink. John Wiley & Sons, Inc.

Zadeh, L.A. (1965). Fuzzy sets. Informations and Control, Vol. 8, 338-353. DOI: https://doi.org/10.1016/S0019-9958(65)90241-X

Zadeh, L.A. (1989). Knowledge representation in fuzzy logic. IEEE Transactions on Knowledge and Data Engineering, Vol. 1, 89-100. DOI: https://doi.org/10.1109/69.43406




How to Cite

Grabusts, P. (2020). FUZZY LOGIC LEARNING METHODS IN STUDENT EDUCATION. SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference, 4, 438-448. https://doi.org/10.17770/sie2020vol4.4840