The Evaluation of Success Degree in Electric Power Engineering Project Based on Principal Component Analysis and Fuzzy Neural Network

被引：3

作者：

Duan, Baoqian ^{[1
]}

Tang, Yun ^{[2
]}

Tian, Li ^{[1
]}

Liu, Qingchao ^{[1
]}

机构：

[1] North China Elect Power Univ, Dept Econ & Management, Baoding 071003, Hebei, Peoples R China

[2] North China Elect Power Univ, Int Cooperat Dept, Baoding 071003, Hebei, Peoples R China

来源：

2008 WORKSHOP ON POWER ELECTRONICS AND INTELLIGENT TRANSPORTATION SYSTEM, PROCEEDINGS | 2008年

关键词：

evaluating degree of success; neural; network principal component analysis; PSO;

D O I：

10.1109/PEITS.2008.9

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Using principal component analysis (PCA) and improved fuzzy neural network by PSO to evaluate the success degree in electric power engineering is this paper's innovative points. First we construct the algorithm model which based on PCA and BP neural network improved by PSO. Secondly using PCA to predigest the given index system and then using the relative membership degree processing the date, which as the input sample of neural network. Thirdly, use the improved BP neural network by PSO to evaluate the success degree of electric power engineering. The result denotes that it is more accuracy and speedily than BP neural network algorithm. Lastly, we give a real engineering, and get a satisfaction result.

引用

页码：339 / +

页数：2

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