Earticle

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

저자

• Ga-eun Lee [ Department of Electronics Engineering Chosun University Gwangju, South Korea ]
• Si-yeon Park [ Department of Electronics Engineering Chosun University Gwangju, South Korea ]
• Gwang-Seop Lee [ Department of Electronics Engineering Chosun University Gwangju, South Korea ]
• Chan-Uk Yeom [ Department of Electronics Engineering Chosun University) Gwangju, South Korea ]

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