NeuroFAST is an on-line fuzzy modeling learning algorithm, featuring high f
unction approximation accuracy and fast convergence. It is based on a first
-order Takagi-Sugeno-Kang (TSK) model, where the consequence part of each f
uzzy rule is a linear equation. Structure identification is performed by a
fuzzy adaptive resonance theory (ART)-like mechanism, assisted by fuzzy rul
e splitting and adding procedures. The well known delta rule continuously p
erforms parameter identification on both premise and consequence parameters
. Simulation results indicate the potential of the algorithm. It is worth n
oting that NeuroFAST achieves a remarkable performance in the Box and Jenki
ns gas furnace process, outperforming all previous approaches compared.