Implementation of Takagi Sugeno Kang Fuzzy with Rough Set Theory and Mini-Batch Gradient Descent Uniform Regularization

The Takagi Sugeno Kang (TSK) fuzzy approach is popular since its output either a constant or function. Parameter identification and structure are the two key requirements for building TSK system. input utilized in can have an impact on number of rules produced such way that employing more data dimensions typically results rules, which causes rule complexity. This issue be solved by dimension reduction technique reduces data. After that, resulting improved with MBGD (Mini-Batch Gradient Descent), then altered uniform regularization (UR). UR enhance classifier's generalization performance. study looks at how rough sets method used to reduce use Mini Batch Descent Uniform Regularization (MBGD-UR) optimize come from TSK. 252 respondents' body fat were as input, mean absolute percentage error (MAPE) was analyze results. Jupyter Notebook software Python programming language processing. analysis revealed MAPE value 37%, falling into moderate area. Doi: 10.28991/ESJ-2023-07-03-09 Full Text: PDF