@Article{cmc.2020.06463,
AUTHOR = {Tao Li, Xiang Li, Yongjun Ren, Jinyue Xia},
TITLE = {Ground Nephogram Recognition Algorithm Based on Selective  Neural Network Ensemble},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {2},
PAGES = {621--631},
URL = {http://www.techscience.com/cmc/v63n2/38533},
ISSN = {1546-2226},
ABSTRACT = {In view of the low accuracy of traditional ground nephogram recognition 
model, the authors put forward a <i>k</i>-means algorithm-acquired neural network ensemble 
method, which takes BP neural network ensemble model as the basis, uses <i>k</i>-means 
algorithm to choose the individual neural networks with partial diversities for integration, 
and builds the cloud form classification model. Through simulation experiments on 
ground nephogram samples, the results show that the algorithm proposed in the article 
can effectively improve the Classification accuracy of ground nephogram recognition in 
comparison with applying single BP neural network and traditional BP AdaBoost 
ensemble algorithm on classification of ground nephogram.},
DOI = {10.32604/cmc.2020.06463}
}