A fast fuzzy modelling approach using clustering neural networks

Proposes a simple and effective method for building a fuzzy model from data. A three-layered RBF network is introduced to implement the fuzzy model. Differing from existing clustering-based methods, in this approach the structure identification of the fuzzy model, including input selection and partition validation, is implemented on the basis of a class of sub-clusters created by a self-organising network instead of on raw data. The important input variables which independently and significantly influence the system output can be extracted by a fuzzy neural network. On the other hand the optimal number of fuzzy rules can be determined separately via the fuzzy c-means algorithm with a modified fuzzy entropy measure as the criterion of cluster validation. The simulation results show that the proposed method can provide good model structure for fuzzy modelling and has high computing efficiency.