Optimization of Fuzzy System Inference Model on Mini Batch Gradient Descent

Surono, Sugiyarto and Thobirin, Aris and Anjani Rafsanjani Hsm, Zani and yuli astuti, Asih and KP, Berlin Ryan and Oktavia, Milla Optimization of Fuzzy System Inference Model on Mini Batch Gradient Descent. FUZZY SYSTEMS AND DATA MINING VIII Proceedings of FSDM 2022.

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Optimization is one of the factors in machine learning to help model
training during backpropagation. This is conducted by adjusting the weights to
minimize the loss function and to overcome dimensional problems. Also, the
gradient descent method is a simple approach in the backpropagation model to solve
minimum problems. The mini-batch gradient descent (MBGD) is one of the methods
proven to be powerful for large-scale learning. The addition of several approaches
to the MBGD such as AB, BN, and UR can accelerate the convergence process,
hence, the algorithm becomes faster and more effective. This added method will
perform an optimization process on the results of the data rule that has been
processed as its objective function. The processing results showed the MBGD-ABBN-UR method has a more stable computational time in the three data sets than the
other methods. For the model evaluation, this research used RMSE, MAE, and

Item Type: Artikel Umum
Subjects: Q Science > Q Science (General)
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: 16 Jun 2023 03:28
Last Modified: 16 Jun 2023 03:28
URI: http://eprints.uad.ac.id/id/eprint/43407

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