GA Optimization of OBF TS Fuzzy Models with Linear and Non Linear Local Models

OBF (Orthonormal Basis Function) Fuzzy models have shown to be a promising approach to the areas of nonlinear system identification and control since they exhibit several advantages over those dynamic model topologies usually adopted in the literature. Although encouraging application results have b...

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Bibliographic Details
Published in2006 Ninth Brazilian Symposium on Neural Networks (SBRN'06) pp. 66 - 71
Main Authors Medeiros, Anderson V., Amaral, Wagner C., Campello, Ricardo J. G. B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2006
Subjects
Automatic control
Control system synthesis
Design optimization
Fuzzy control
Fuzzy systems
Genetic algorithms
Nonlinear control systems
Nonlinear dynamical systems
Nonlinear systems
Topology
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Summary:OBF (Orthonormal Basis Function) Fuzzy models have shown to be a promising approach to the areas of nonlinear system identification and control since they exhibit several advantages over those dynamic model topologies usually adopted in the literature. Although encouraging application results have been obtained, no automatic procedure had yet been developed to optimize the design parameters of these models. This paper elaborates on the use of a genetic algorithm (GA) especially designed for this task, in which a fitness function based on the Akaike information criterion plays a key role by considering both model accuracy and parsimony aspects. The use of linear (actually affine) and nonlinear local models is also investigated. The proposed methodology is evaluated in the modeling of a real nonlinear magnetic levitation system.
ISBN:9780769526805
0769526802
ISSN:1522-4899
2375-0235
DOI:10.1109/SBRN.2006.20