Research Article of Journal of Theoretical and Applied Sciences
Abnormal data cleaning in thermal power plant based on self-organizing maps
Song Yu1, Guozhu Chen2*, Zhou Bao1, Baogang Song2
1.College of Computer and Information Technology, China Three Gorges University,China.
2.College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang, 443002, China.
This paper constructs a self-organizing maps (SOM) neural network model for the anomaly data cleaning in thermal power plant detection. The test data is trained 2000 times so that the vector of each weight is located at the center of the input vector cluster, and the 6*6 competitive network is constructed. The network classifies or eliminates the screening of data, and obtains a healthy sample library that can be used to predict the running state of the machine in the future, achieving a good data cleaning effect.
Keywords: Self-organizing Map; Data cleaning
How to cite this article:
Song Yu, Guozhu Chen, Zhou Bao, Baogang SonG. Abnormal data cleaning in thermal power plant based on self-organizing maps. Journal of Theoretical and Applied Sciences, 2019, 2:10. DOI:10.28933/jtas-2019-02-18051
References:
1. Minling Zhang, Zhaogan Chen, Zhihua Zhou.
A Survey of SOM Algorithm, LVQ Algorithm and Its Variants[J]. Computer Science, 2002(07): 97-100.
2. Yufei Yue and Jianxu Luo. Application of an Improved SOM Neural Network in Fault Di-agnosis of Wastewater Treatment[J]. Journal of East China University of Science and Technology, 2017,43(03):389-396.
3. Jinping Yu and Qin Xu. Data Cleaning Technology Based on SOM Network Clus-tering [J]. Science and Technology Plaza, 2005 (08): 59-61.
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