DocumentCode
501357
Title
Notice of Retraction
Application Study on Performance Evaluation for Engineering Project Management Based on BP Neural Network
Author
Jing Xu ; Jianguo Chen
Author_Institution
Sch. of Econ. & Manage., Tongji Univ., Shanghai, China
Volume
1
fYear
2009
fDate
15-17 May 2009
Firstpage
393
Lastpage
396
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In order to make the performance evaluation for engineering project management quantitative and scientific, this paper adopts BP network method of artificial neural network to copy with the problem. By analysis, we set up a quantitative model of performance evaluation including of four main control indicators for engineering project management, and apply the model in an example. From the example we can see the method is more overall and more objective, which will promote the level of engineering project management improving.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In order to make the performance evaluation for engineering project management quantitative and scientific, this paper adopts BP network method of artificial neural network to copy with the problem. By analysis, we set up a quantitative model of performance evaluation including of four main control indicators for engineering project management, and apply the model in an example. From the example we can see the method is more overall and more objective, which will promote the level of engineering project management improving.
Keywords
backpropagation; civil engineering computing; neural nets; BP network; artificial neural network; backpropagation; engineering project management; project performance evaluation; Artificial neural networks; Data engineering; Engineering management; Fuzzy control; Information technology; Neural networks; Performance analysis; Project management; Technology management; Utility theory; BP neural network; control indicator; performance evaluation;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Technology and Applications, 2009. IFITA '09. International Forum on
Conference_Location
Chengdu
Print_ISBN
978-0-7695-3600-2
Type
conf
DOI
10.1109/IFITA.2009.508
Filename
5231635
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