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Neural Computing and Applications

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01.12.2013 | Original Article

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

verfasst von: Choon Ki Ahn, Myo Taeg Lim

Erschienen in: Neural Computing and Applications | Sonderheft 1/2013

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Abstract

This paper investigates the model predictive stabilization problem for 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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Titel: Model predictive stabilizer for T–S fuzzy recurrent multilayer neural network models with general terminal weighting matrix
verfasst von: Choon Ki Ahn
Myo Taeg Lim
Publikationsdatum: 01.12.2013
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1381-3

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