A new type of recurrent fuzzy neural network for modeling dynamic systems

被引：59
|
作者：
Zhou, SM
Xu, LD
机构：
[1] Chinese Acad Sci, Remote Sensing Satellite Ground Stn, Div Remote Sensing Informat Proc, Beijing 100086, Peoples R China
[2] Wright State Univ, Dept Management Sci & Informat Syst, Dayton, OH 45435 USA
[3] Natl Key Lab Mfg Syst Engn, Xian 710049, Peoples R China
来源：
KNOWLEDGE-BASED SYSTEMS | 2001年 / 14卷 / 5-6期
关键词：
fuzzy modeling; recurrent neural network; fuzzy control; dynamic system;
D O I：
10.1016/S0950-7051(01)00102-2
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a new type of neural network called recurrent fuzzy neural network (RFNN) is proposed to model the fuzzy dynamical systems (FDS). FDS is considered as an order system. The network developed in this paper is based on recurrent neural networks (RNN) to capture the dynamical properties of FDS. The training algorithm is derived based on the tool of order derivative. An example is given to demonstrate the validity of the approach. (C) 2001 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：243 / 251
页数：9
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