Dynamic system identification using a Type-2 Recurrent Fuzzy Neural Network
Chia‐Feng Juang, Yang-Yin Lin, I‐Fang Chung · Asian Control Conference · 2009
This paper proposes an Interval Type-2 Recurrent Fuzzy Neural Network (IT2RFNN) for dynamic system identification. The antecedent parts in each recurrent fuzzy rule in the IT2RFNN are interval type-2 fuzzy sets, and the consequent part is of the Takagi-Sugeno-Kang (TSK) type with interval weights. The recurrent structure in the T2RFNN enables it to handle dynamic system identification problems with a priori knowledge of system input and output delay numbers. A T2RFNN is constructed using concurrent structure and parameter learning. Simulations on dynamic system identification with clean and noisy outputs verify the performance of T2RFNN.