The 10th International Conference on Next Generation Computing 2024 (2024.11)바로가기
페이지
pp.375-377
저자
Ga-eun Lee, Si-yeon Park, Gwang-Seop Lee, Chan-Uk Yeom
언어
영어(ENG)
URL
https://www.earticle.net/Article/A468889
원문정보
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
키워드
ANFISGrid PartitioningSubtractive ClusteringFCM
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 ]