Earticle

초록

영어

This study compared the performance of the FCM(C-Means)-based ANFIS(Adaptive Neuro-Fuzzy Inference System) model and the CFCM(Context-based Fuzzy C-Means) clustering-based ANFIS model. The FCM-ANFIS model sets the initial Fuzzy Rule through FCM clustering and optimizes the rule through neural network learning. The CFCM-ANFIS model generates more sophisticated rules through CFCM clustering that considers the input and output variable space and learns the neural network. As a result of the experiment, the verification RMSE of the FCM-based ANFIS model was 3.5654 when the number of clusters was 6, and the RMSE of the CFCM clustering-based ANFIS model was 3.3954 in the parameters (P = 6, C = 2), which was higher than the FCM-based ANFIS model. It was confirmed that the CFCM method had better prediction performance than the FCM method, and this study proved that the CFCM-based ANFIS model was more effective in predicting body fat percentage.

목차

Abstract
I. INTRODUCTION
II. EXISTING FIS GENERATION METHOD
A. Fuzzy C-Means Clustering
B. FCM-ANFIS
III. CFCM-AFIS
A. Context-based Fuzzy C-Means Clustering
B. CFCM-ANFIS
IV. EXPERIMENT
A. Database
B. RMSE
C. Experimental Method
V. CONCLUSION
REFERENCES

키워드

저자

• Si-yeon Park [ Department of Electronics Engineering Chosun University Gwangju, South Korea ]
• Ga-eun Lee [ Department of Electronics Engineering Chosun University Gwangju, South Korea ]
• Gwang-seop Lee [ Department of Electronics Engineering Chosun University Gwangju, South Korea ]
• Chan-Uk Yeom [ Division of AI Convergence College Chosun University Gwangju, South Korea ]
• Keun-Chang Kwak [ Dept. of Electronics Engineering Chosun University Gwangju, Republic of Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004