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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.
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Source :
Information and Control
ISSN: 1002-0411
Year: 2014
Issue: 1
Volume: 43
Page: 49-55
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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