An efficient recurrent neuro-fuzzy system for identification and control of dynamic systems

This paper presents a self-adaptive recurrent neuro-fuzzy inference system (R-SANFIS) for dealing with dynamic problems. The proposed recurrent system possesses two salient features: 1) it incorporates fuzzy basis functions (FBFs) with dynamic elements for better approximation of nonlinear dynamic functions, and 2) it is capable of translating the complicated behaviors of dynamic systems into a set of simple linguistic "dynamic" rules and state-space equations as well. A systematic self-adaptive learning algorithm has been developed to construct the R-SANFIS with a parsimonious network structure and fast parameter learning convergence. Computer simulations and the performance comparisons with some existing recurrent networks on identification and control of nonlinear dynamic systems have been conducted to validate the effectiveness of the proposed R-SANFIS.