Based On K-Means and Nearest Neighbor Algorithm for Fuzzy System Used for Data Fitting

3rd International Conference on Automation, Control and Robotics Engineering

工业技术;计算机科学;无线电电子学

Hu, Yi^1 ; Han, Jixia^1 ; Dian, Songyi^1

College of Electrical Engineering and Information Technology, Sichuan University, Chengdu, China^1

关键词: Data fittings;    K-means;    k-Means algorithm;    Nearest neighbor algorithm;    Number of clusters;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/428/1/012038/pdf
DOI  :  10.1088/1757-899X/428/1/012038

来源: IOP

【 摘 要 】

This paper proposes using classical clustering algorithm in fuzzy system to fit experimental data. The main purpose is to reduce the number of fuzzy rules by clustering. K-means algorithm and nearest neighbor algorithm clusters classify data into classes so that each cluster corresponds to a fuzzy rule, the number of fuzzy rules is also determined by the number of clusters. We compare and discuss the results of data fitting between K-means algorithm and nearest neighbor algorithm. It find that two algorithm both can achieve better data fitting performance.