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Ground Nephogram Recognition Algorithm Based on Selective Neural Network Ensemble

Tao Li1, Xiang Li1, *, Yongjun Ren2, Jinyue Xia3
1 College of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
2 College of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
3 International Business Machines Corporation (IBM), New York, USA.
* Corresponding Author: Xiang Li. Email: .

Computers, Materials & Continua 2020, 63(2), 621-631.

Received 26 February 2019; Accepted 13 April 2019; Issue published 01 May 2020


In view of the low accuracy of traditional ground nephogram recognition model, the authors put forward a k-means algorithm-acquired neural network ensemble method, which takes BP neural network ensemble model as the basis, uses k-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.


Cloud shape, k-means, BP neural network, adaboost.

Cite This Article

T. Li, X. Li, Y. Ren and J. Xia, "Ground nephogram recognition algorithm based on selective neural network ensemble," Computers, Materials & Continua, vol. 63, no.2, pp. 621–631, 2020.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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