Fuzzy Neural Identification by Online Clustering with Application on Crude Oil Blending

In this paper we propose a novel online clustering approach which can be applied for nonlinear system modeling. Fuzzy neural networks are used as models whose structure and parameters are updated online. The new idea for the structure identification is that the input (precondition) and the output (consequent) spaces partitioning are carried out in the same time index. This idea gives better explanation for input-output mapping of nonlinear system. An application on modeling of crude oil blending is proposed.