scholarly journals Integrated GUI Environment of Parallel Fuzzy Inference System for Pattern Classification of Remote Sensing Images

2002 ◽  
Vol 2 (2) ◽  
pp. 133-138
Author(s):  
Seong-Hoon Lee ◽  
Sang-Gu Lee ◽  
Ki-Sung Son ◽  
Jong-Hyuk Kim ◽  
Byung-Kwon Lee
Keyword(s):  
Remote Sensing ◽  
Pattern Classification ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Remote Sensing Images ◽  
Inference System

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

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 Detection and classification of bearing faults in industrial geared motors using temporal features and adaptive neuro-fuzzy inference system

Heliyon ◽  
2019 ◽  
Vol 5 (8) ◽  
pp. e02046 ◽  
Author(s):  
Choug Abdelkrim ◽  
Mohamed Salah Meridjet ◽  
Nadir Boutasseta ◽  
Lakhdar Boulanouar
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Bearing Faults ◽  
Temporal Features ◽  
Neuro Fuzzy
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