Self-organising T-S fuzzy Elman network based on EKF

For the design of the fuzzy neural network architecture and the deficiency of fuzzy sets on semantic description, a self-organising T-S fuzzy Elman network (SOTSFEN) based on extended Kalman filter (EKF) is proposed, and the training algorithm is derived. Furthermore, recursive least square (RLS) and EKF are used to update linear and non-linear parameters respectively. Then the criterion of rule generation is given and error ratio reduction (ERR) is regarded as the fuzzy rule pruning strategy. Finally, the simulation results of system identification and sewage treatment modeling show that the precision and generalization ability of SOTSFEN are ensured, and a simpler architecture network can be achieved simultaneously.