NeuroFAST: On-line neuro-fuzzy ART-based structure and parameter learning TSK model

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.