关键词: 模糊规则, 中智模糊聚类, 基于背景的模糊聚类, 加权最小二乘方法

Abstract:

Reinforced rule-based fuzzy model has obvious limitation for noisy data sets. To solve this drawback, a reinforced rule-based fuzzy model based on neutrosophic C-means clustering (NCM-RbF) is proposed. First of all, neutrosophic C-means clustering (NCM) algorithm is inspired from fuzzy C-means and the neutrosophic set framework. The NCM clustering algorithm is used to generate the initial rules of the reinforced rule-based fuzzy (RRbF) models, then the context fuzzy C-means clustering (CFCM) algorithm is used to generate the new rules of the RRbF models, and the reinforced rule-based fuzzy based on neutrosophic C-means clustering (NCM-RRbF) model is obtained. The proposed model has satisfactory anti-noise effect, which is suitable for clustering problem with boundary data points and noisy data points. For a systematic experiment of adding Gauss white noise with different noise intensity to artificial data sets and real data sets, the experimental results show that the proposed model has promising approximation capability and strong robustness for noisy data modeling tasks.

Key words: fuzzy rule, neutrosophic C-means clustering, context fuzzy C-means clustering, weighted least square estimation