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Multidimensional membership functions in T–S fuzzy models for modelling and identification of nonlinear multivariable systems using genetic algorithms

AuthorsAdánez García-Villaraco, José Miguel; Al-Hadithi, Basil M.; Jiménez Avello, Agustín
KeywordsFuzzy rules
Takagi–Sugeno model
Genetic algorithm
Multidimensional membership functions
Issue DateFeb-2019
PublisherElsevier BV
CitationApplied Soft Computing 75: 607-615 (2019)
AbstractIn this work, a new method for Takagi-Sugeno (T-S) fuzzy modelling based on multidimensional membership functions (MDMFs) is proposed. It is verified that the fuzzy inference method of one-dimensional membership functions (lDMFs) may place the fuzzy rules in inappropriate locations for modelling of nonlinear multivariable systems, while the application of MDMFs allows a better identification through a smaller number of fuzzy rules. The proposed method uses a genetic algorithm (GA) for the adjustment of the MDMFs and the T-S method for modelling and identification of the nonlinear system. As a validation example, a nonlinear multivariable system, a coupled tanks system, is chosen. The results show that the proposed method presents less identification error than the T-S method, with less number of fuzzy rules.
Publisher version (URL)https://doi.org/10.1016/j.asoc.2018.11.034
Appears in Collections:(CAR) Artículos
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