Self-organizing Recurrent Fuzzy Neural Network for Nonlinear System Modeling

Recurrent fuzzy neural network (RFNN) are widely used with nonlinear system modeling. However, the modeling ability of RFNN is usually compromised due to the presence of uncertain external disturbances and changing unknown environments. To address this problem, a self-organizing recurrent fuzzy neural network with modified Levenberg-Marquardt algorithm (MLM-SORFNN) is proposed for nonlinear systems modeling. Firstly, a dynamic adjustment mechanism of the network structure based on correntropy is proposed to improve the network ability to adapt to uncertain environments. Secondly, an improved LM algorithm with adaptive learning rate is designed, which can improve the modeling accuracy while ensuring the convergence of the network. Finally, the experimental results demonstrate the superior modeling capability of the MLM-SORFNN.