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

2002 ◽  
Vol 21 (12) ◽  
pp. 1193-1196 ◽  
Author(s):  
Lin Zhang ◽  
Al Fortier ◽  
David C. Bartel
Keyword(s):  
Neural Network ◽  
Self Organizing Map ◽  
Self Organizing

Classification of Indian power coals using K-means clustering and Self Organizing Map neural network

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
Keyword(s):  
Neural Network ◽  
Self Organizing Map ◽  
Self Organizing

Classification of Heat Wave Events in Seoul Using Self-Organizing Map

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

scholarly journals Classification of chosen orchard pests using the self-organizing feature maps neural network

2012 ◽  
Vol 7 (47) ◽  
pp. 6357-6362 ◽  
Author(s):  
Pilarski Krzysztof ◽  
Boniecki Piotr ◽  
Slosarz Piotr ◽  
Dach Jacek ◽  
Boniecka Piekarska Hanna ◽  
...  
Keyword(s):  
Neural Network ◽  
The Self ◽  
Feature Maps ◽  
Orchard Pests ◽  
Self Organizing

scholarly journals Application of self organizing map in construction, geology and petroleum industry

2017 ◽  
Vol 20 (K4) ◽  
pp. 30-38
Author(s):  
Tung Son Pham ◽  
Huy Minh Truong ◽  
Tuan Ba Pham
Keyword(s):  
Industrial Revolution ◽  
Human Life ◽  
Petroleum Industry ◽  
Self Organizing Map ◽  
Practical Applications ◽  
Fourth Industrial Revolution ◽  
Som Algorithm ◽  
Clustering Data ◽  
Self Organizing

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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