Classification of Ordovician Tight Reservoir Facies in Algeria by Using Neuro-Fuzzy Algorithm

2021 ◽  
pp. 889-895
Author(s):  
M. Z. Doghmane ◽  
S. A. Ouadfeul ◽  
Z. Benaissa ◽  
S. Eladj
Keyword(s):  
Fuzzy Algorithm ◽  
Tight Reservoir ◽  
Neuro Fuzzy ◽  
Reservoir Facies

Adaptive Neuro-Fuzzy Inference System for Classification of ACL-Ruptured Knees Using Arthrometric Data

2008 ◽  
Vol 36 (9) ◽  
pp. 1449-1457 ◽  
Author(s):  
Zoya Heydari ◽  
Farzam Farahmand ◽  
Hossein Arabalibeik ◽  
Mohamad Parnianpour
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

NEURO-FUZZY BASED SEARCH ROBOT FOR SOFTWARE COMPONENTS

1999 ◽  
Vol 08 (02) ◽  
pp. 119-135
Author(s):  
YAU-HWANG KUO ◽  
JANG-PONG HSU ◽  
MONG-FONG HORNG
Keyword(s):  
Retrieval System ◽  
Software Component ◽  
Software Components ◽  
Personalized Search ◽  
Component Retrieval ◽  
Reusable Software ◽  
Major Mechanism ◽  
Fuzzy Models ◽  
Neuro Fuzzy

A personalized search robot is developed as one major mechanism of a personalized software component retrieval system. This search robot automatically finds out the Web servers providing reusable software components, extracts needed software components from servers, classifies the extracted components, and finally establishes their indexing information for local component retrieval in the future. For adaptively tuning the performance of software component extraction and classification, an adaptive thesaurus and an adaptive classifier, realized by neuro-fuzzy models, are embedded in this search robot, and their learning algorithms are also developed. A prototype of the personalized software component retrieval system including the search robot has been implemented to confirm its validity and evaluate the performance. Furthermore, the framework of proposed personalized search robot could be extended to the search and classification of other kinds of Internet documents.

GPS Signal Reception Classification Using Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Vol 72 (3) ◽  
pp. 685-701 ◽  
Author(s):  
Rui Sun ◽  
Li-Ta Hsu ◽  
Dabin Xue ◽  
Guohao Zhang ◽  
Washington Yotto Ochieng
Keyword(s):  
Fuzzy Inference System ◽  
Urban Areas ◽  
Fuzzy Inference ◽  
Receiver Design ◽  
Single Variable ◽  
Gps Measurements ◽  
Inference System ◽  
Static Test ◽  
Neuro Fuzzy

The multipath effect and Non-Line-Of-Sight (NLOS) reception of Global Positioning System (GPS) signals both serve to degrade performance, particularly in urban areas. Although receiver design continues to evolve, residual multipath errors and NLOS signals remain a challenge in built-up areas. It is therefore desirable to identify direct, multipath-affected and NLOS GPS measurements in order improve ranging-based position solutions. The traditional signal strength-based methods to achieve this, however, use a single variable (for example, Signal to Noise Ratio (C/N0)) as the classifier. As this single variable does not completely represent the multipath and NLOS characteristics of the signals, the traditional methods are not robust in the classification of signals received. This paper uses a set of variables derived from the raw GPS measurements together with an algorithm based on an Adaptive Neuro Fuzzy Inference System (ANFIS) to classify direct, multipath-affected and NLOS measurements from GPS. Results from real data show that the proposed method could achieve rates of correct classification of 100%, 91% and 84%, respectively, for LOS, Multipath and NLOS based on a static test with special conditions. These results are superior to the other three state-of-the-art signal reception classification methods.

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.

scholarly journals Classification Of Wheat Grains Using Image Processing Techniques Based Neuro-Fuzzy System Model

2018 ◽  
pp. 57-61
Author(s):  
Ahmet Kayabasi ◽  
Kadir Sabanci ◽  
Abdurrahim Toktas
Keyword(s):  
Image Processing ◽  
Fuzzy System ◽  
Absolute Error ◽  
Data Set ◽  
Wheat Grains ◽  
Image Processing Techniques ◽  
Neuro Fuzzy ◽  
Length Width ◽  
Processing Techniques

In this study, an image processing techniques (IPTs) and a Sugeno-typed neuro-fuzzy system (NFS) model is presented for classifying the wheat grains into bread and durum. Images of 200 wheat grains are taken by a high resolution camera in order to generate the data set for training and testing processes of the NFS model. The features of 5 dimensions which are length, width, area, perimeter and fullness are acquired through using IPT. Then NFS model input with the dimension parameters are trained through 180 wheat grain data and their accuracies are tested via 20 data. The proposed NFS model numerically calculate the outputs with mean absolute error (MAE) of 0.0312 and classify the grains with accuracy of 100% for the testing process. These results show that the IPT based NFS model can be successfully applied to classification of wheat grains.

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