摘要
为了实现自动建立Mamdani模糊模型,提出了一种基于局部数据密度的新方法.该方法采用局部近似隶属函数的模糊聚类算法对数据进行学习,从而挖掘出潜在的模糊规则集和隶属函数的参数,实现自动建立Mamdani模糊模型.在聚类时,不需要事先指定类的数目,确定类中心的同时能自动识别噪声,因此在建模时不需要做额外的去噪声处理.使用该方法对交通信息预测进行了仿真实验,结果表明本文提出的模糊建模方法行之有效.
To automatically construct a Mamdani fuzzy model,a novel approach is proposed based on local density of data.The fuzzy rule base and membership function parameters for a candidate fuzzy system can be determined through the data mining using the clustering algorithm of fuzzy clustering of local approximation of membership（FLAME）,and consequently the fuzzy system is generated automatically.In the clustering process,there is no requirement to specify the number of clusters and the outliers can be automatically identified without any extra pre-processing.The proposed approach is evaluated through a set of simulated experiments on the traffic prediction and the results indicate that the proposed approach for fuzzy system identification is feasible and efficient.
出处
《北京工业大学学报》
EI
CAS
CSCD
北大核心
2012年第2期257-261,共5页
Journal of Beijing University of Technology
基金
北京市教育委员会科技发展计划面上项目资助(KM201010005021)
博士科研启动基金资助项目(X0004011200903)
关键词
模糊聚类
模糊推理系统
模糊建模
噪声识别
行驶速度
fuzzy clustering
fuzzy inference system
fuzzy system identification
outliers identification
travel speed prediction
