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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: email.

Computers, Materials & Continua 2020, 63(2), 621-631. https://doi.org/10.32604/cmc.2020.06463

Abstract

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.

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Cite This Article

APA Style
Li, T., Li, X., Ren, Y., Xia, J. (2020). Ground nephogram recognition algorithm based on selective neural network ensemble. Computers, Materials & Continua, 63(2), 621-631. https://doi.org/10.32604/cmc.2020.06463
Vancouver Style
Li T, Li X, Ren Y, Xia J. Ground nephogram recognition algorithm based on selective neural network ensemble. Comput Mater Contin. 2020;63(2):621-631 https://doi.org/10.32604/cmc.2020.06463
IEEE Style
T. Li, X. Li, Y. Ren, and J. Xia "Ground Nephogram Recognition Algorithm Based on Selective Neural Network Ensemble," Comput. Mater. Contin., vol. 63, no. 2, pp. 621-631. 2020. https://doi.org/10.32604/cmc.2020.06463



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