Determination Method of Nonlinear Membership Function Based on the Fuzzy Density Means Cluster

Liu Qi,Chen Zhanwei,Chen Xi,Wang Jie,Wu Zhenjie,Liu Xuelin

doi:10.19026/rjaset.5.4691

Abstract

Gaussian and Sigmoid membership function are commonly used, both of which have well smoothness, clear physical meaning and have no zeros in their figures. In this paper, fuzzy density means clustering method based on data distance is put forward, by using this method, the initial cluster center is obtained objectively, which has avoided the cluster results falling into a local minimum, finally the parameters used for describe the membership function are got. Through simulation, the membership function of data EB_Da51 in petroleum drilling is determined, which solves the problem that the function is hard to be defined.

Highlights

The phenomenon and things with uncertainty are widespread in the nature and human society
Fuzzy clustering method is the base of many classification problems and system modeling, the purpose of fuzzy clustering is to extract the inherent characteristics from a large number, to obtain the compact representation of the system behavior
A fuzzy density-mean clustering method based on data distance is proposed, which is simple and easy to spread, by using this method, nonlinear membership function of variable of total volume in petroleum drilling (Zhao et al, 2009; Fred, 2005; Li et al, 2009) is determined

Summary

Introduction

The phenomenon and things with uncertainty are widespread in the nature and human society. A fuzzy density-mean clustering method based on data distance is proposed, which is simple and easy to spread, by using this method, nonlinear membership function of variable of total volume in petroleum drilling (Zhao et al, 2009; Fred, 2005; Li et al, 2009) is determined.

Results

Conclusion