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Author:

Yuan, X. (Yuan, X..) | Han, H. (Han, H..) | Qiao, J. (Qiao, J..)

Indexed by:

Scopus PKU CSCD

Abstract:

A dynamic T-S (Takgi-Sugeno) fuzzy Elman network (DTSFEN) , which has the advantages of the T-S fuzzy model and Elman network, is proposed This DTSFEN can process dynamic information with a globally convergent recursive structure Moreover, an adaptive error back-propagation learning algorithm is designed to update the structure parameters and the fuzzy rule parameters, which can improve the learning efficiency of DTSFEN Then, the global convergence of the DTSFEN is proven using the Lyapunov stability theorem Finally, the DTSFEN is used for nonlinear function approximation and sludge volume index (SYT) soft sensor The experimental results show that the DTSFEN has a faster convergence rate and better accuracy and robustness than the orthogonal least squares (OLS) and Elman network.

Keyword:

Convergence; Dynamic T-S fuzzy Elman network (DTSFEN); Neural network; Soft sensor

Author Community:

  • [ 1 ] [Yuan, X.]College of Electronic Infomiation and Control Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Han, H.]College of Electronic Infomiation and Control Engineering, Beijing University of Technology, Beijing 100124, China
  • [ 3 ] [Qiao, J.]College of Electronic Infomiation and Control Engineering, Beijing University of Technology, Beijing 100124, China

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Source :

Information and Control

ISSN: 1002-0411

Year: 2014

Issue: 1

Volume: 43

Page: 49-55

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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