An improved TSK-type recurrent fuzzy network for dynamic system identification

In this paper, we propose an improved TSK-type recurrent fuzzy network (ITRFN) for dynamic system identification. Due to the improper clustering method and the restriction of first-order internal dynamics, the original TRFN has a poor representation capability and becomes inefficient for high-order temporal problems. To improve the previous deficiencies, we propose a new incremental self-clustering method to initialize the network structure and weights in the structure learning phase. Our clustering method can generate clusters that fit the real data distribution better than the original TRFN. Besides, we extend the internal dynamics to be high-order, and add adaptive parameters for tuning the membership functions of internal variables. These extensions make the ITRFN more general and flexible. Experimental results have shown that our method can achieve a higher precision with less training time than the original TRFN.