### 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.

Original language | English |
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Pages (from-to) | 271-277 |

Number of pages | 7 |

Journal | Neural Computing and Applications |

Volume | 23 |

Issue number | SUPPL1 |

DOIs | |

Publication status | Published - 2013 Mar 27 |

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### Keywords

- Cost monotonicity
- Linear matrix inequality (LMI)
- Model predictive stabilization
- Takagi-Sugeno (T-S) fuzzy neural networks

### ASJC Scopus subject areas

- Artificial Intelligence
- Software

### Cite this

**Model predictive stabilizer for T-S fuzzy recurrent multilayer neural network models with general terminal weighting matrix.** / Ahn, Choon Ki; Lim, Myo Taeg.

Research output: Contribution to journal › Article

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TY - JOUR

T1 - Model predictive stabilizer for T-S fuzzy recurrent multilayer neural network models with general terminal weighting matrix

AU - Ahn, Choon Ki

AU - Lim, Myo Taeg

PY - 2013/3/27

Y1 - 2013/3/27

N2 - 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.

AB - 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.

KW - Cost monotonicity

KW - Linear matrix inequality (LMI)

KW - Model predictive stabilization

KW - Takagi-Sugeno (T-S) fuzzy neural networks

UR - http://www.scopus.com/inward/record.url?scp=84888819586&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84888819586&partnerID=8YFLogxK

U2 - 10.1007/s00521-013-1381-3

DO - 10.1007/s00521-013-1381-3

M3 - Article

AN - SCOPUS:84888819586

VL - 23

SP - 271

EP - 277

JO - Neural Computing and Applications

JF - Neural Computing and Applications

SN - 0941-0643

IS - SUPPL1

ER -