Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems

2005 ◽  
Vol 13 (1) ◽  
pp. 140-151 ◽  
Author(s):  
L. Rutkowski ◽  
K. Cpalka
Keyword(s):  
Fuzzy Systems ◽  
Triangular Norms ◽  
Neuro Fuzzy

NEURO-FUZZY SYSTEMS APPLIED ON A FIXED-WING AIRCRAFT CONTROL

2019 ◽  
Author(s):  
Renata Bernardes ◽  
Bruno Luiz Pereira ◽  
Felipe Machini Malachias Marques ◽  
Roberto Mendes Finzi Neto
Keyword(s):  
Fuzzy Systems ◽  
Aircraft Control ◽  
Neuro Fuzzy

scholarly journals A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis

2012 ◽  
Vol 05 (07) ◽  
pp. 477-482 ◽  
Author(s):  
Rafik Mahdaoui ◽  
Leila Hayet Mouss
Keyword(s):  
Fuzzy Systems ◽  
Neuro Fuzzy ◽  
Fault Prognosis

scholarly journals Modeling of the magnetizing phenomena of doubly fed induction generator using neuro-fuzzy algorithm considering non-linearity

2019 ◽  
Vol 9 (1) ◽  
pp. 23
Author(s):  
Julia Tholath Jose ◽  
Adhir Baran Chattopadhyay
Keyword(s):  
Fuzzy Systems ◽  
Short Circuit ◽  
Sudden Change ◽  
Continuous Functions ◽  
Input Power ◽  
Induction Generator ◽  
Doubly Fed Induction Generator ◽  
Fuzzy Algorithm ◽  
Neuro Fuzzy ◽  
Doubly Fed

Doubly fed Induction Generators (DFIGs) are quite common in wind energy conversion systems because of their variable speed nature and the lower rating of converters. Magnetic flux saturation in the DFIG significantly affect its behavior during transient conditions such as voltage sag, sudden change in input power and short circuit. The effect of including saturation in the DFIG modeling is significant in determining the transient performance of the generator after a disturbance. To include magnetic saturation in DFIG model, an accurate representation of the magnetization characteristics is inevitable. This paper presents a qualitative modeling for magnetization characteristics of doubly fed induction generator using neuro-fuzzy systems. Neuro-fuzzy systems with one hidden layer of Gaussian nodes are capable of approximating continuous functions with arbitrary precision. The results obtained are compared with magnetization characteristics obtained using discrete fourier transform, polynomial and exponential curve fitting. The error analysis is also done to show the effectiveness of the neuro fuzzy modeling of magnetizing characteristics. By neuro-fuzzy algorithm, fast learning convergence is observed and great performance in accuracy is achieved.

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