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Model predictive stabilizer for T-S fuzzy recurrent multilayer neural network models with general terminal weighting matrix

Authors
Ahn, Choon KiLim, Myo Taeg
Issue Date
12월-2013
Publisher
SPRINGER LONDON LTD
Keywords
Model predictive stabilization; Takagi-Sugeno (T-S) fuzzy neural networks; Cost monotonicity; Linear matrix inequality (LMI)
Citation
NEURAL COMPUTING & APPLICATIONS, v.23, pp.S271 - S277
Indexed
SCIE
SCOPUS
Journal Title
NEURAL COMPUTING & APPLICATIONS
Volume
23
Start Page
S271
End Page
S277
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/101509
DOI
10.1007/s00521-013-1381-3
ISSN
0941-0643
Abstract
This paper investigates the model predictive stabilization problemfor Takagi-Sugeno (T-S) fuzzy multilayer neural networks with general terminal weighting matrix. A new set of linear matrix inequality (LMI) conditions on the general terminal weighting matrix of receding horizon cost function is presented such that T-S fuzzy multilayer neural networks with model predictive stabilizer are asymptotically stable. The general terminal weighting matrix of receding horizon cost function can be obtained by solving a set of LMIs. A numerical example is given to illustrate the effectiveness of the proposed stabilization scheme.
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공과대학 (전기전자공학부)
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