A New Fault Diagnosis Model of Electric Power Grid Based on Rough Set and Neural Network

被引：3

作者：

Zhang Liying ^{[1
]}

Wang Dazhi ^{[1
]}

Zhang Cuiling ^{[1
]}

Liu Xiaoqin ^{[1
]}

机构：

[1] Northeastern Univ, Sch Informat Sci & Engn, Shenyang, Peoples R China

来源：

2012 FOURTH INTERNATIONAL CONFERENCE ON MULTIMEDIA INFORMATION NETWORKING AND SECURITY (MINES 2012) | 2012年

关键词：

electric power grid; fault diagnosis; rough sets; neural networks; membership function;

D O I：

10.1109/MINES.2012.37

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Fault diagnosis for system quick return to normal after the accident has important significance. On the basis of giving a new type of attribute reduction method, a coupling recognition model is established which combines rough set and neural network closely in this paper. It used rough set theory to get the most smiple decision rules from the data samples, to guide to establish neural network structure. Using rough membership function initializes the network parameters, in order to reduce the network training iterative times and improve the network convergence speed. The simulation results illustrate that the model improves network's structure, and its recognizing effects are obvious and its classifying ability is strong, as well as the model is very error permissible and explicable. It has very wide foreground.

引用

页码：405 / 408

页数：4

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