Classification of salt‐contaminated velocities with self‐organizing map neural network

2001 ◽  
Author(s):  
Lin Zhang ◽  
David C. Bartel ◽  
Al Fortier
2002 ◽  
Vol 21 (12) ◽  
pp. 1193-1196 ◽  
Author(s):  
Lin Zhang ◽  
Al Fortier ◽  
David C. Bartel

Fuel ◽  
2011 ◽  
Vol 90 (1) ◽  
pp. 339-347 ◽  
Author(s):  
Yogesh P. Pandit ◽  
Yogesh P. Badhe ◽  
B.K. Sharma ◽  
Sanjeev S. Tambe ◽  
Bhaskar D. Kulkarni

2018 ◽  
Vol 9 (3) ◽  
pp. 209-221 ◽  
Author(s):  
Seung-Yoon Back ◽  
Sang-Wook Kim ◽  
Myung-Il Jung ◽  
Joon-Woo Roh ◽  
Seok-Woo Son

2012 ◽  
Vol 7 (47) ◽  
pp. 6357-6362 ◽  
Author(s):  
Pilarski Krzysztof ◽  
Boniecki Piotr ◽  
Slosarz Piotr ◽  
Dach Jacek ◽  
Boniecka Piekarska Hanna ◽  
...  

2017 ◽  
Vol 20 (K4) ◽  
pp. 30-38
Author(s):  
Tung Son Pham ◽  
Huy Minh Truong ◽  
Tuan Ba Pham

In recent years, Artificial Intelligence (AI) has become an emerging subject and been recognized as the flagship of the Fourth Industrial Revolution. AI is subtly growing and becoming vital in our daily life. Particularly, Self-Organizing Map (SOM), one of the major branches of AI, is a useful tool for clustering data and has been applied successfully and widespread in various aspects of human life such as psychology, economic, medical and technical fields like mechanical, construction and geology. In this paper, the primary purpose of the authors is to introduce SOM algorithm and its practical applications in geology and construction. The results are classification of rock facies versus depth in geology and clustering two sets of construction prices indices and building material costs indice.


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