The Clustering Algorithm for Nonlinear System Identification

A new on-line clustering fuzzy neural network is proposed. In the algorithm, structure and parameter learning are updated at the same time. There is not difference between structure learning and parameter learning. It generates groups with a given radius. The center is updated in order to get that the center is near to the incoming data in each iteration, in this way, It does not need to generate a new rule in each iteration, i.e., it does not generate many rules and it does not need to prune the rules. Key-Words: Clustering algorithm, Fuzzy systems, Modeling, Identification.