MATEC Web of Conferences
Volume 22, 2015International Conference on Engineering Technology and Application (ICETA 2015)
|Number of page(s)||7|
|Section||Information and Communication Technology|
|Published online||09 July 2015|
- Chao, Liu & Zhengyou, He & Jianwei, Yang. 2008. A Quantum Neural Network Based Fault Diagnosis Algonthm for Power Grid. Grid technology. 32(9): 56–60. [Google Scholar]
- Xu, Zhang & Juan, Wei & Dongmei, Zhao. 2013. Research Course and Prospects of Power Grid Fault Diagnosis. Grid technology. 37(10): 2745–2753. [Google Scholar]
- Li, Bian & Chenyuan, Bian. 2014. Review on intelligence fault diagnosis in power networks. Power Protection and Control System. 42(3): 146–153. [Google Scholar]
- 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] [Google Scholar]
- 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. [Google Scholar]
- 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. [Google Scholar]
- Jiaqiang, E. 2006. Intelligent Fault Diagnosis and Its Applications. Changsha: Hunan University Press. [Google Scholar]
- 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. [Google Scholar]
- 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. [Google Scholar]
- McCall, J. 2005. Genetic algorithms for modelling and optimisation. Journal of Computational and Applied Mathematics. 1(184): 205–222. [NASA ADS] [CrossRef] [Google Scholar]
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