Article Dans Une Revue IEEE Transactions on Fuzzy Systems Année : 2008

Parameter Identification of Recurrent Fuzzy Systems with Fuzzy Finite-State Automata Representation

Résumé

This paper presents the identification of non-linear dynamical systems by recurrent fuzzy system models. Two types of recurrent fuzzy systems (RFS) models are discussed, the Takagi-Sugeno-Kang (TSK) type and the linguistic or Mamdani type. Both models are equivalent and the latter model may be represented by a fuzzy finite-state automaton. An identification procedure is proposed based on a standard general purpose genetic algorithm. First, the TSK rule parameters are estimated and, in a second step, the TSK model is converted into an equivalent linguistic model. The parameter identification is evaluated in some benchmark problems for non-linear system identification described in literature. The results show that RFS models achieve good numerical performance while keeping the interpretability of the actual system dynamics.

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hal-00139493 , version 1 (31-03-2007)

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Carlos A. Gama, Alexandre Evsukoff, Philippe Weber, Nelson F. F. Ebecken. Parameter Identification of Recurrent Fuzzy Systems with Fuzzy Finite-State Automata Representation. IEEE Transactions on Fuzzy Systems, 2008, 16 (1), pp.213-224. ⟨10.1109/TFUZZ.2007.902015⟩. ⟨hal-00139493⟩
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