Open Access
Issue
MATEC Web of Conferences
Volume 22, 2015
International Conference on Engineering Technology and Application (ICETA 2015)
Article Number 01050
Number of page(s) 7
Section Information and Communication Technology
DOI https://doi.org/10.1051/matecconf/20152201050
Published online 09 July 2015
  1. Chao, Liu & Zhengyou, He & Jianwei, Yang. 2008. A Quantum Neural Network Based Fault Diagnosis Algonthm for Power Grid. Grid technology. 32(9): 56–60.
  2. Xu, Zhang & Juan, Wei & Dongmei, Zhao. 2013. Research Course and Prospects of Power Grid Fault Diagnosis. Grid technology. 37(10): 2745–2753.
  3. Li, Bian & Chenyuan, Bian. 2014. Review on intelligence fault diagnosis in power networks. Power Protection and Control System. 42(3): 146–153.
  4. Ding, S.F. & Yu, J.Z. 2011. An optimizing BP neural network algorithm based on genetic algorithm. Artificial Intelligence Review. 36(2): 153–162. [CrossRef]
  5. Ningsheng, Gong. 2011. AB network adjust the step and the hidden-layer neurons algorithm based on BP network. 13th IEEE Joint International Computer Science and Information Technology Conference (JICSIT 2011). Chongqing: IEEE.
  6. Yangming, Guo & Congbao, Ran & Xinyu, Ji. 2013. Analogous circuit fault diagnosis based on combinatorial optimization BP neural network. Northwestern Polytechnical University Journal. 31(1): 45–48.
  7. Jiaqiang, E. 2006. Intelligent Fault Diagnosis and Its Applications. Changsha: Hunan University Press.
  8. Chuangxin, Guo & Chuanbo, Zhu & Yijia, Cao. 2006. Research status and development trend of the power system fault diagnosis. Automation of Electric Power Systems. 30(8): 98–103.
  9. Hong, Yan & Yanping, Guan. 2009. Method to Determine the Quantity of Internal Nodes of Back Propagation Neural Networks and Its Demonstration. Control Engineering of China. 16(S1): 100–102.
  10. McCall, J. 2005. Genetic algorithms for modelling and optimisation. Journal of Computational and Applied Mathematics. 1(184): 205–222. [NASA ADS] [CrossRef]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.