文献类型：期刊文献

中文题名：神经模糊系统中模糊规则的优选

英文题名：Optimal choice of fuzzy rules in neuro-fuzzy systems

作者：贾立[1];俞金寿[1]

机构：[1]华东理工大学自动化研究所,上海200237

年份：2002

卷号：17

期号：3

起止页码：306

中文期刊名：控制与决策

外文期刊名：Control and Decision

收录：CSTPCD;;EI(收录号：2002397108483);Scopus;北大核心:【北大核心2000】；CSCD:【CSCD2011_2012】；

语种：中文

中文关键词：神经模糊系统;模糊规则;聚类算法;人工神经网络

外文关键词：neuro fuzzy system; two stage clustering algorithm; proved nearest neighborhood clustering algorithm; GK fuzzy clustering algorithm; fuzzy partition entropy

摘要：提出一种基于两级聚类算法的自组织神经模糊系统 ,该系统采用两级聚类算法 (改进的最近邻域聚类算法和 Gustafson- Kessel模糊聚类算法 )对输入 /输出数据进行模糊聚类 ,并由模糊聚类的划分熵确定最优划分 ,建立模糊模型 ,模型精度可由梯度下降法进一步提高。仿真结果表明 ,这种神经模糊系统具有结构简单、规则数少。
A self organizing neuro fuzzy system based on two stage clustering algorithm is proposed. Two stage clustering algorithm consisting of the nearest neighborhood clustering algorithm and Gustafson Kessel fuzzy clustering algorithm with cluster validity criteria is used to partition the input output space. The optimal number of fuzzy rules can be determined via fuzzy entropy as the criterion of cluster validation. A supervised scheme is utilized for constructing more optimal fuzzy model. Two simulation results show that the proposed method can provide optimal model structure and parameters for fuzzy modeling and possesses high learning efficiency.