DocumentCode :
2972261
Title :
The Evaluation of Success Degree in Electric Power Engineering Project Based on Principal Component Analysis and Fuzzy Neural Network
Author :
Duan, Baoqian ; Tang, Yun ; Tian, Li ; Liu, Qingchao
Author_Institution :
Dept. of Econ. & Manage., North China Electr. Power Univ., Baoding
fYear :
2008
fDate :
2-3 Aug. 2008
Firstpage :
339
Lastpage :
344
Abstract :
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.
Keywords :
backpropagation; fuzzy neural nets; particle swarm optimisation; power engineering computing; principal component analysis; BP neural network; PSO; electric power engineering project; fuzzy neural network; particle swarm optimisation; principal component analysis; Clustering algorithms; Fuzzy neural networks; Intelligent networks; Intelligent transportation systems; Investments; Neural networks; Power electronics; Power engineering and energy; Principal component analysis; Project management; PSO; evaluating degree of success; neural network; principal component analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Electronics and Intelligent Transportation System, 2008. PEITS '08. Workshop on
Conference_Location :
Guangzhou
Print_ISBN :
978-0-7695-3342-1
Type :
conf
DOI :
10.1109/PEITS.2008.9
Filename :
4634872
Link To Document :
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