FUZZY LOGIC LEARNING METHODS IN STUDENT EDUCATION

Authors

  • Peter Grabusts Rezekne Academy of Technologies

DOI:

https://doi.org/10.17770/sie2020vol4.4840

Keywords:

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

Abstract

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.

 

References

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Published

2020-05-20