Extended Kalman Filter Based Learning Algorithm for Type-2 Fuzzy Logic Systems and Its Experimental Evaluation

In this paper, the use of extended Kalman filter for the optimization of the parameters of type-2 fuzzy logic systems is proposed. The type-2 fuzzy logic system considered in this study benefits from a novel type-2 fuzzy membership function which has certain values on both ends of the support and th...

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Veröffentlicht in:	IEEE transactions on industrial electronics (1982) 2012-11, Vol.59 (11), p.4443-4455
Hauptverfasser:	Khanesar, M. A., Kayacan, E., Teshnehlab, M., Kaynak, O.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Antilock braking system (ABS) Artificial neural networks Computational efficiency Extended Kalman filter extended Kalman filter (EKF) Feedback control feedback error learning (FEL) Fuzzy Fuzzy logic Fuzzy set theory Fuzzy sets identification Kalman filters Learning Noise measurement Optimization Studies Trains type-2 fuzzy logic systems (T2FLSs) type-2 fuzzy neural network Uncertainty
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