An Enhanced Online Self-organizing Fuzzy Neural Network

An Enhanced Online Self-organizing Fuzzy Neural Network (EOS-FNN) is proposed in this paper. The proposed algorithm can improve computational efficiency while achieving comparable performance and accuracy compared to other methods. The proposed EOS-FNN starts with an empty rule set and automatically generates fuzzy rules according to the proposed criteria during the learning process. All the parameters of the fuzzy rules are updated by the Extended Kalman Filter (EKF) method. Nonlinear time-series prediction processes are used to evaluate the performance of the proposed EOS-FNN algorithm with a comparison to other popular algorithms including DFNN, GDFNN and FAOS-PFNN. Simulation results have shown that the proposed algorithm reduces computation time while achieving comparable accuracy.