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

Surono, Sugiyarto (2023) Implementation of Takagi Sugeno Kang Fuzzy with Rough Set Theory And Mini-Batch Gradient Descent Uniform Regularization. Emerging Science Journal (ISSN: 2610-91, 7 (3). ISSN ISSN: 2610-9182

Text (HASIL CEK SIMILARITY)
similaritas esj1400.pdf - Accepted Version
Download (1MB)

Official URL: https://www.researchgate.net/publication/370672394...

Abstract

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

Item Type:	Artikel Umum
Subjects:	Q Science > QA Mathematics
Divisi / Prodi:	Faculty of Applied Science and Technology (Fakultas Sains Dan Teknologi Terapan) > S1-Mathematics (S1-Matematika)
Depositing User:	Dr Sugiyarto Surono
Date Deposited:	14 Jun 2023 01:54
Last Modified:	14 Jun 2023 01:54
URI:	http://eprints.uad.ac.id/id/eprint/43375

Actions (login required)

View Item