文献类型：期刊文献

中文题名：A Multilayer Recurrent Fuzzy Neural Network for Accurate Dynamic System Modeling

英文题名：A multilayer recurrent fuzzy neural network for accurate dynamic system modeling

作者：Liu, He[1]; Huang, Dao[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

年份：2008

卷号：25

期号：4

起止页码：373

中文期刊名：Journal of Donghua University(English Edition)

外文期刊名：Journal of Donghua University (English Edition)

收录：EI(收录号：20090611897392);Scopus

语种：英文

中文关键词：recurrent neural networks; T-S fuzzy model;chaotic search ; least square estimation ; modeling

外文关键词：Multilayers - Fuzzy neural networks - Fuzzy inference

摘要：A multilayer recurrent fuzzy neural network(MRFNN)is proposed for accurate dynamic system modeling.The proposed MRFNN has six layers combined with T-S fuzzy model.The recurrent structures are formed by local feedback connections in the membership layer and the rule layer.With these feedbacks,the fuzzy sets are time-varying and the temporal problem of dynamic system can be solved well.The parameters of MRFNN are learned by chaotic search(CS)and least square estimation(LSE)simultaneously,where CS is for tuning the premise parameters and LSE is for updating the consequent coefficients accordingly.Results of simulations show the proposed approach is effective for dynamic system modeling with high accuracy.
A multilayer recurrent fuzzy neural network (MRFNN) is proposed for accurate dynamic system modeling. The proposed MRFNN has six layers combined with T-S fuzzy model. The recurrent structures are formed by local feedback connections in the membership layer and the rule layer. With these feedbacks, the fuzzy sets are time-varying and the temporal problem of dynamic system can be solved well. The parameters of MRFNN are learned by chaotic search (CS) and least square estimation (LSE) simultaneously, where CS is for tuning the premise parameters and LSE is for updating the consequent coefficients accordingly. Results of simulations show the proposed approach is effective for dynamic system modeling with high accuracy.