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

被引：0

作者：

Qiao, Jun-Fei ^{[1
]}

Yuan, Xi-Chun ^{[1
]}

Han, Hong-Gui ^{[1
]}

机构：

[1] School of Electric Information and Control Engineering, Beijing University of Technology

来源：

Kongzhi yu Juece/Control and Decision | 2014年 / 29卷 / 05期

关键词：

Error ratio reduction; Extended Kalman filter; Function approximation; Self-organising T-S fuzzy Elman network; Soft sensor;

D O I：

10.13195/j.kzyjc.2013.0116

中图分类号：

学科分类号：

摘要：

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.

引用

页码：853 / 859

页数：6

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