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Model predictive stabilizer for T-S fuzzy recurrent multilayer neural network models with general terminal weighting matrix
- Authors
- Ahn, Choon Ki; Lim, Myo Taeg
- Issue Date
- Dec-2013
- Publisher
- SPRINGER LONDON LTD
- Keywords
- Model predictive stabilization; Takagi-Sugeno (T-S) fuzzy neural networks; Cost monotonicity; Linear matrix inequality (LMI)
- Citation
- NEURAL COMPUTING & APPLICATIONS, v.23, pp.S271 - S277
- Indexed
- SCIE
SCOPUS
- Journal Title
- NEURAL COMPUTING & APPLICATIONS
- Volume
- 23
- Start Page
- S271
- End Page
- S277
- ISSN
- 0941-0643
- Abstract
- This paper investigates the model predictive stabilization problemfor 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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Related Researcher
- Ahn, Choon ki
- College of Engineering (School of Electrical Engineering)