Takagi–Sugeno fuzzy models in the framework of orthonormal basis functions

Machado, Jeremias B., Campello, Ricardo, and Amaral, Wagner C. (2013) Takagi–Sugeno fuzzy models in the framework of orthonormal basis functions. IEEE Transactions on Cybernetics, 43 (3). pp. 858-870.

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An approach to obtain Takagi-Sugeno (TS) fuzzy models of nonlinear dynamic systems using the framework of orthonormal basis functions (OBFs) is presented in this paper. This approach is based on an architecture in which local linear models with ladder-structured generalized OBFs (GOBFs) constitute the fuzzy rule consequents and the outputs of the corresponding GOBF filters are input variables for the rule antecedents. The resulting GOBF-TS model is characterized by having only real-valued parameters that do not depend on any user specification about particular types of functions to be used in the orthonormal basis. The fuzzy rules of the model are initially obtained by means of a well-known technique based on fuzzy clustering and least squares. Those rules are then simplified, and the model parameters (GOBF poles, GOBF expansion coefficients, and fuzzy membership functions) are subsequently adjusted by using a nonlinear optimization algorithm. The exact gradients of an error functional with respect to the parameters to be optimized are computed analytically. Those gradients provide exact search directions for the optimization process, which relies solely on input-output data measured from the system to be modeled. An example is presented to illustrate the performance of this approach in the modeling of a complex nonlinear dynamic system.

Item ID: 47673
Item Type: Article (Research - C1)
ISSN: 2168-2275
Keywords: generalized orthonormal basis functions (OBFs) (GOBFs), nonlinear systems, system identification, Takagi–Sugeno(TS) fuzzy models
Date Deposited: 10 Mar 2017 02:38
FoR Codes: 01 MATHEMATICAL SCIENCES > 0102 Applied Mathematics > 010299 Applied Mathematics not elsewhere classified @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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