Application of Self-organizing Feature Map Neural Network Based on Data Clustering

被引：0

作者：

Hu, Xiang ^{[1
]}

Yang, Yun ^{[2
]}

Zhang, Lihong ^{[1
]}

Xiang, Tao ^{[1
]}

Hong, Chengqiu ^{[2
]}

Zheng, Xiaotong ^{[2
]}

机构：

[1] Hubei Elect Power Co, Dispatching & Commun Ctr, Wuhan, Hubei, Peoples R China

[2] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE 10TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA 2012) | 2012年

关键词：

artificial neural network; self-organizing feature map; clustering algorithm;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Outlier detection is of much importance in preprocessing of data collected from complex industry system, for the data has strong nonlinearity and poor stability, involving much noise. Outlier detection based on clustering, rejects abnormal data points which have significant difference from others according to the definition of similarity. Self-organizing Feature Map (SOM) Neural Network algorithm has the self-study and adaptive functions of neural networks, so as to be a hot research in clustering analysis recently. This paper first introduces Self-organizing Feature Map algorithm based on artificial neural network, and then improves the algorithm by using weighted Euclidean distance, finally uses the software of MATLAB to analyze some actual data of electrical power. The result shows that SOM algorithm achieves a very good effect in clustering, and the MATLAB toolbox shows favorable visual effects.

引用

页码：797 / 802

页数：6