A generalized Takagi–Sugeno–Kang recurrent fuzzy-neural filter for adaptive noise cancelation
Δεν υπάρχει διαθέσιμη μικρογραφία
Ημερομηνία
2008-10
Τίτλος Εφημερίδας
Περιοδικό ISSN
Τίτλος τόμου
Εκδότης
Δικαιώματα
Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές
Άδειες
Παραπομπή
Παραπομπή
Περίληψη
This paper presents a recurrent fuzzy-neural filter for adaptive noise cancelation. The cancelation task is transformed to a system-identification problem, which is tackled by use of the dynamic neuron-based fuzzy neural network (DN-FNN). The fuzzy model is based on Takagi–Sugeno–Kang fuzzy rules, whose consequent parts consist of linear combinations of dynamic neurons. The orthogonal least squares method is employed to select the number of rules, along with the number and kind of dynamic neurons that participate in each rule. Extensive simulation results are given and performance comparison with a series of other dynamic fuzzy and neural models is conducted, underlining the effectiveness of the proposed filter and its superior performance over its competing rivals.