DocumentCode
518215
Title
Notice of Retraction
The application of genetic neural network in prediction of building subsidence
Author
Li Xipan ; Zhang Ling ; Hu Jing
Author_Institution
Hebei Univ. of Eng., Handan, China
Volume
3
fYear
2010
fDate
16-18 April 2010
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.
Using genetic algorithm and error back-propagation algorithm combining algorithm to training artificial neural network. First, using genetic algorithms global training. Second, using BP algorithm to doing accurate training. In order to make the network convergence faster and avoid falling into local minima. This article combines BP neural network with genetic algorithm and establish prediction model of genetic neural network. Combined with the measured data we forecasted the building subsidence. The predicted results show that the use of improved hybrid model can improve precision of the result.
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.
Using genetic algorithm and error back-propagation algorithm combining algorithm to training artificial neural network. First, using genetic algorithms global training. Second, using BP algorithm to doing accurate training. In order to make the network convergence faster and avoid falling into local minima. This article combines BP neural network with genetic algorithm and establish prediction model of genetic neural network. Combined with the measured data we forecasted the building subsidence. The predicted results show that the use of improved hybrid model can improve precision of the result.
Keywords
backpropagation; genetic algorithms; neural nets; structural engineering computing; artificial neural network training; building subsidence prediction; error back-propagation algorithm; genetic algorithm; genetic neural network; network convergence; prediction model; Artificial neural networks; Biological cells; Buildings; Convergence; Genetic algorithms; Genetic engineering; Neural networks; Predictive models; Safety; Signal processing; BP neural network algorithm; genetic algorithm(GA); modeling; prediction;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5485859
Filename
5485859
Link To Document