원문정보
한국차세대컴퓨팅학회
한국차세대컴퓨팅학회 학술대회
The 10th International Conference on Next Generation Computing 2024
2024.11
pp.375-377
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
This study analyzes the performance of ANFIS(Adaptive Neuro-Fuzzy Inference System) based on the input space partitioning method. Using body fat datasets and concrete compressive strength datasets, various fuzzy system configuration methods, such as Grid Partitioning, Subtractive Clustering, and FCM(Fuzzy C-Means), are compared. The results show that the FCM-based ANFIS model demonstrated superior performance, recording the lowest RMSE value. It is confirmed that the initialization method of the fuzzy system significantly influences the performance of ANFIS, and the optimal configuration method may vary depending on the data distribution and complexity.
목차
Abstract
I. INTRODUCTION
II. RELATED RESEARCH
A. Fuzzy set
B. Grid partitioning-based ANFIS
C. Subtractive Clustering-based ANFIS
D. Fuzzy C-Means Clustering based-ANFIS
III. EXPERIMENTAL RESULTS AND ANALYSIS
A. Dataset
B. Performance evaluation
C. Experimental methods and results
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES
I. INTRODUCTION
II. RELATED RESEARCH
A. Fuzzy set
B. Grid partitioning-based ANFIS
C. Subtractive Clustering-based ANFIS
D. Fuzzy C-Means Clustering based-ANFIS
III. EXPERIMENTAL RESULTS AND ANALYSIS
A. Dataset
B. Performance evaluation
C. Experimental methods and results
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES
저자정보
참고문헌
자료제공 : 네이버학술정보