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

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4 Author(s)
Baoqian Duan ; Dept. of Econ. & Manage., North China Electr. Power Univ., Baoding ; Yun Tang ; Li Tian ; Qingchao Liu

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.

Published in:

Power Electronics and Intelligent Transportation System, 2008. PEITS '08. Workshop on

Date of Conference:

2-3 Aug. 2008