基于模糊聚类建立模糊模型的新方法

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.

Novel Approach to Fuzzy System Identification Based on Fuzzy Clustering

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.