Modified recurrent neuro-fuzzy network for modeling ball-screw servomechanism by using Chebyshev polynomial

This paper proposes a Chebyshev functional recurrent neuro-fuzzy (CFRNF) network to identify a nonlinear system, which is composed of nine layers network and a six-layer Chebyshev recurrent neural network (CRNN) used to emulate nonlinear system is one of nine layers. Based on Takagi–Sugeno–Kang (TSK) fuzzy model, the nonlinear dynamics of this system can be addressed by enhancing the input dimensions of the consequent parts in the fuzzy rules due to functional expansion of a Chebyshev polynomial. The back propagation algorithm is used to adjust the parameters of the antecedent membership functions as well as those of consequent functions. real system of ball-screw servomechanism with nonlinearity of stick-slip motion, the analytical and experimental results indicate that the accuracy and convergence of the CFRNF are superior to those of the identification results by adaptive neural fuzzy inference system (ANFIS) and recurrent neural network (RNN).