DocumentCode
1877076
Title
Notice of Retraction
Application of Simulated Annealing BP Neural Network in Financial Crisis Early Warning
Author
Pan Shuang ; Yu Wei-kang ; Gao Mei ling
Author_Institution
Sch. of Econ., Wuhan Univ. of Technol., Wuhan, China
fYear
2010
fDate
10-12 Dec. 2010
Firstpage
1
Lastpage
3
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.
The establishment financial crisis early warning system has the very vital significance to national economy´s construction, the financial crisis is a complex non-linear problem, the present model is based on the linear method, but the neural network is suitable to the processing non-linear problem. This paper improves the BP algorithm with the simulation annealing algorithm, effectively overcomes the shortcoming of the BP algorithm to easily fall into the local minimum, enhances the BP network performance, and will fuse the algorithm to apply forewarns in the financial crisis, the findings indicated that the fusion algorithm´s performance is highly effective, the forecast is precise.
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.
The establishment financial crisis early warning system has the very vital significance to national economy´s construction, the financial crisis is a complex non-linear problem, the present model is based on the linear method, but the neural network is suitable to the processing non-linear problem. This paper improves the BP algorithm with the simulation annealing algorithm, effectively overcomes the shortcoming of the BP algorithm to easily fall into the local minimum, enhances the BP network performance, and will fuse the algorithm to apply forewarns in the financial crisis, the findings indicated that the fusion algorithm´s performance is highly effective, the forecast is precise.
Keywords
backpropagation; economic forecasting; financial management; neural nets; simulated annealing; BP network performance; BP neural network; complex nonlinear problem; early warning system; financial crisis; national economy construction; simulated annealing; Artificial neural networks; Biological system modeling; Economic indicators; Markov processes; Neurons; Simulated annealing;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Software Engineering (CiSE), 2010 International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-5391-7
Type
conf
DOI
10.1109/CISE.2010.5677039
Filename
5677039
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