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

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12 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

Keywords

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

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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