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

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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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Authors and Affiliations

  1. School of Electrical Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 136-701, Korea

    Choon Ki Ahn & Myo Taeg Lim

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  1. Choon Ki Ahn
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  2. Myo Taeg Lim
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Correspondence to Choon Ki Ahn.

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Ahn, C.K., Lim, M.T. Model predictive stabilizer for T–S fuzzy recurrent multilayer neural network models with general terminal weighting matrix. Neural Comput & Applic 23 (Suppl 1), 271–277 (2013). https://doi.org/10.1007/s00521-013-1381-3

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  • DOI: https://doi.org/10.1007/s00521-013-1381-3

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