A compensation-based recurrent fuzzy neural network for dynamic system identification

In this paper, a type of compensation-based recurrent fuzzy neural network (CRFNN) for identifying dynamic systems is proposed. The proposed CRFNN uses a compensation-based fuzzy reasoning method, and has feedback connections added in the rule layer of the CRFNN. The compensation-based fuzzy reasoning method can make the fuzzy logic system more adaptive and effective, and the additional feedback connections can solve temporal problems. The CRFNN model is proven to be a universal approximator in this paper. Moreover, an online learning algorithm is proposed to automatically construct the CRFNN. The results from simulations of identifying dynamic systems have shown that the convergence speed of the proposed method is faster than the convergence speed of conventional methods and that only a small number of tuning parameters are required.