EEG features extraction using neuro-fuzzy systems and shift-invariant wavelet transforms for epileptic seizures diagnosing

2005 ◽  
Author(s):  
A. Akhbardeh ◽  
M. Farrokhi ◽  
A. Vahabian Tehrani
Keyword(s):  
Wavelet Transforms ◽  
Fuzzy Systems ◽  
Epileptic Seizures ◽  
Features Extraction ◽  
Neuro Fuzzy ◽  
Eeg Features

scholarly journals Wavelet Transforms based Fall Detection with Neuro-Fuzzy Systems based Feature Selection

2019 ◽  
Vol 8 (3) ◽  
pp. 7498-7502
Keyword(s):  
Feature Selection ◽  
Wavelet Transforms ◽  
Fuzzy Systems ◽  
Fall Detection ◽  
Wavelet Coefficients ◽  
Distribution Measurement ◽  
Neuro Fuzzy ◽  
Area Distribution ◽  
Minimum Number ◽  
Overlap Area

This study proposes a method to detect fall with minimum features selected by a non-overlap area distribution measurement (NADM) method. In preprocessing step, wavelet transforms were carried out to extract wavelet coefficients from dataset acquired by subjects. The NADM was used to select the minimum number of features from wavelet coefficients, and then 19 features were finally selected from the 33 features. The performance result of the fall detection was tested with 19 features, and then the sensitivity, accuracy, and specificity were shown to be 95%, 96.13%, and 97.25%, respectively

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.

Modeling research on the sorption kinetics of pentachlorophenol (PCP) to sediments based on neural networks and neuro-fuzzy systems

2007 ◽  
Vol 20 (2) ◽  
pp. 239-247 ◽  
Author(s):  
Xiao-kang Su ◽  
Guang-ming Zeng ◽  
Guo-he Huang ◽  
Jian-bing Li ◽  
Jie Liang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fuzzy Systems ◽  
Sorption Kinetics ◽  
Neuro Fuzzy ◽  
Kinetics Of

Merging Ensemble of Neuro-Fuzzy Systems

2006 ◽  
Author(s):  
M. Korytkowski ◽  
R. Nowicki ◽  
L. Rutkowski ◽  
R. Scherer
Keyword(s):  
Fuzzy Systems ◽  
Neuro Fuzzy
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