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.