Towards hybrid 2D phase unwrapping using fuzzy clustering and neuro-fuzzy learning for SAR images: a case study on IFSAR phase image

2002 ◽  
Author(s):  
P.T.H. Hui ◽  
Wang Yin Chai
Keyword(s):  
Fuzzy Clustering ◽  
Phase Unwrapping ◽  
Phase Image ◽  
Sar Images ◽  
Neuro Fuzzy ◽  
Fuzzy Learning

scholarly journals Introducing a software for innovative neuro-fuzzy clustering method named NFCMR

2018 ◽  
Vol 8 (2) ◽  
pp. 62-69 ◽  
Author(s):  
Aref Shirazi ◽  
Adel Shirazy ◽  
Shahab Saki ◽  
Ardeshir Hezarkhani
Keyword(s):  
Neural Network ◽  
Fuzzy Clustering ◽  
Mineral Exploration ◽  
General Regression Neural Network ◽  
Clustering Method ◽  
Neuro Fuzzy ◽  
Fuzzy Clustering Method ◽  
Fuzzy C Means Clustering ◽  
General Regression

An innovative neural-fuzzy clustering method is for predicting cluster (anomaly / background) of each new sample with the probability of its presence. This method which is a combination of the Fuzzy C-Means clustering method (FCM) and the General Regression Neural Network (GRNN), is an attempt to first divide the samples in the region by fuzzy method with the probability of being in each cluster and then with the results of this Practice, the artificial neural network is trained, and can analyze the new data entered in the region with the probable percentage of the clusters. More clearly, after a full mineral exploration, the sample can be attributed to a certain probable percentage of anomalies. To test the accuracy of this clustering in the form of the theory alone, a case study was conducted on the results of the analysis of regional alluvial sediments data in Birjand, IRAN, which resulted in satisfactory results. This software is written in MATLAB and its first application in mining engineering. This algorithm can be used in other similar applications in various sciences.

Segmenting SAR Images Using Fuzzy Clustering

1999 ◽  
Author(s):  
Paul R. Kersten ◽  
Roger R. Lee ◽  
Jim S. Verdi ◽  
Ron M. Carvlho ◽  
Stephen P. Yankovich
Keyword(s):  
Fuzzy Clustering ◽  
Sar Images

Transfer of knowledge in chemical equipment reliability

1989 ◽  
Vol 54 (10) ◽  
pp. 2692-2710 ◽  
Author(s):  
František Babinec ◽  
Mirko Dohnal
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chemical Equipment ◽  
Transfer Of Knowledge ◽  
Equipment Reliability ◽  
Fuzzy Clustering Algorithm

The problem of transformation of data on the reliability of chemical equipment obtained in particular conditions to other equipment in other conditions is treated. A fuzzy clustering algorithm is defined for this problem. The method is illustrated on a case study.

Flood Mapping Using Multi-temporal Sentinel-1 SAR Images: A Case Study—Inaouene Watershed from Northeast of Morocco

2021 ◽  
Author(s):  
Brahim Benzougagh ◽  
Pierre-Louis Frison ◽  
Sarita Gajbhiye Meshram ◽  
Larbi Boudad ◽  
Abdallah Dridri ◽  
...  
Keyword(s):  
Flood Mapping ◽  
Sar Images ◽  
Multi Temporal

scholarly journals An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering

2016 ◽  
Vol 8 (5) ◽  
pp. 12-18 ◽  
Author(s):  
Zhengbing Hu ◽  
◽  
Yevgeniy V. Bodyanskiy ◽  
Oleksii K. Tyshchenko ◽  
Olena O. Boiko
Keyword(s):  
Fuzzy Clustering ◽  
Data Stream ◽  
Online Data ◽  
Neuro Fuzzy ◽  
Kohonen Networks

An Integrated Data Mining and Simulation Solution

2010 ◽  
pp. 929-948
Author(s):  
Mouhib Alnoukari ◽  
Asim El Sheikh ◽  
Zaidoun Alzoabi
Keyword(s):  
Data Mining ◽  
Fuzzy Inference ◽  
Decision Support Tools ◽  
Market Demand ◽  
Inference System ◽  
Car Market ◽  
Neuro Fuzzy ◽  
Support Tools ◽  
Quality In Higher Education

Simulation and data mining can provide managers with decision support tools. However, the heart of data mining is knowledge discovery; as it enables skilled practitioners with the power to discover relevant objects and the relationships that exist between these objects, while simulation provides a vehicle to represent those objects and their relationships. In this chapter, the authors will propose an intelligent DSS framework based on data mining and simulation integration. The main output of this framework is the increase of knowledge. Two case studies will be presented, the first one on car market demand simulation. The simulation model was built using neural networks to get the first set of prediction results. Data mining methodology used named ANFIS (Adaptive Neuro-Fuzzy Inference System). The second case study will demonstrate how applying data mining and simulation in assuring quality in higher education

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