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The Scientific World Journal
Volume 2014, Article ID 582042, 11 pages
http://dx.doi.org/10.1155/2014/582042
Research Article

Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

1College of Computer, National University of Defense Technology, Changsha 410073, China
2Xiangyang School for NCOs, Xiangyang 441118, China

Received 6 May 2014; Accepted 16 July 2014; Published 12 August 2014

Academic Editor: K. I. Ramachandran

Copyright © 2014 Hong Yin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [3 citations]

The following is the list of published articles that have cited the current article.

  • Lina Chato, Shahab Tayeb, and Shahram Latifi, “A genetic algorithm to optimize the adaptive Support Vector Regression model for forecasting the reliability of diesel engine systems,” 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), pp. 1–7, . View at Publisher · View at Google Scholar
  • Hongxia Cai, Tingting Yu, and Chenglong Xia, “Quality-Oriented Classification of Aircraft Material Based on SVM,” Mathematical Problems in Engineering, vol. 2014, pp. 1–12, 2014. View at Publisher · View at Google Scholar
  • Shenghan Zhou, Silin Qian, Wenbing Chang, Yiyong Xiao, and Yang Cheng, “A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier,” Sensors, vol. 18, no. 6, pp. 1934, 2018. View at Publisher · View at Google Scholar