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

Research output: Contribution to journalArticle

10 Citations (Scopus)

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 languageEnglish
Pages (from-to)271-277
Number of pages7
JournalNeural Computing and Applications
Volume23
Issue numberSUPPL1
DOIs
Publication statusPublished - 2013 Mar 27

Fingerprint

Recurrent neural networks
Multilayer neural networks
Fuzzy neural networks
Cost functions
Stabilization
Linear matrix inequalities

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

@article{5917c6cc979b47208083db0491e61925,
title = "Model predictive stabilizer for T-S fuzzy recurrent multilayer neural network models with general terminal weighting matrix",
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.",
keywords = "Cost monotonicity, Linear matrix inequality (LMI), Model predictive stabilization, Takagi-Sugeno (T-S) fuzzy neural networks",
author = "Ahn, {Choon Ki} and Lim, {Myo Taeg}",
year = "2013",
month = "3",
day = "27",
doi = "10.1007/s00521-013-1381-3",
language = "English",
volume = "23",
pages = "271--277",
journal = "Neural Computing and Applications",
issn = "0941-0643",
publisher = "Springer London",
number = "SUPPL1",

}

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 -