RBF-SVM AND ITS APPLICATION ON RELIABILITY EVALUATION OF ELECTRIC POWER SYSTEM COMMUNICATION NETWORK

摘要：

Support Vector Machine (SVM) is a novel machine learning method after the artificial neural networks (ANN). The SVM with RBF is the research hot spot in assessment area at present Because of its good learning performance, the SVM with RBF is widely used in practical application. In this paper, the RBF-SVM and its application on reliability evaluation of electric power system communication network is researched. Through experiments, the impact of learning ability and generalization ability for the error penalty parameter Cand kernel function width σ is analyzed and compared, how the parameters affect the performance of RBF-SVM is expatiated, the pictures of the changing curve that the parameters Cand σ affect the number of Support Vector(SV) and wrong recognition rate are presented. AT last, through reliability evaluation with SVM under different kernel function, compare with their assessment performance, and the performance superiority of RBF-SVM is validated.

关键词： RBF-SVM Kernel function Electric power system communication network Indicator system Reliability evaluation

作者: ZHEN-DONG ZHAO YUN-YONG LOU JUN-HONG NI JING ZHANG

作者单位: Department of Electronic and Communication Engineering, North China Electric Power University, Baoding, 071002, China

会议类型: 国际会议

会议名称: 2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

会议地点: 保定

会议语种:英文

页码: 1188-1193

在线出版日期: 2009-07-12（万方平台首次上网日期，不代表论文的发表时间）