DocumentCode
527439
Title
Notice of Retraction
A neural network model for deformation prediction of deep foundation pit based on multivariate phase space reconstruction
Author
Xuefeng Xi ; Anyong Li ; Jianmin Ban ; Weizhong Lu
Author_Institution
Sch. of Electron. & Inf. Eng., Suzhou Univ. of Sci. & Technol., Suzhou, China
Volume
3
fYear
2010
fDate
10-12 Aug. 2010
Firstpage
1493
Lastpage
1497
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 civil engineering, synthesized deformation prediction of deep foundation pit is a very complicated problem. For it belongs to a multi-variable nonlinear system evolution with time-varying, an enhanced back propagation (BP) neural network model based on multi-variable phase-space reconstruction has been proposed. By the various time series time delay and embedding dimension determined respectively in this model, the multi-variable series of excavation deformation for deep foundation pit has been done in the first phase space reconstruction. The neural network input extraction by the use of partial least squares regression method can be the strongest impact components. Finally non-linear fitting between the various components has been completed via the enhanced BP neural network model. With practical application for deformation prediction of deep foundation pit, the method´s effectiveness has been verified. In addition, compared with the traditional BP network, enhanced BP neural network has better convergence and accuracy.
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 civil engineering, synthesized deformation prediction of deep foundation pit is a very complicated problem. For it belongs to a multi-variable nonlinear system evolution with time-varying, an enhanced back propagation (BP) neural network model based on multi-variable phase-space reconstruction has been proposed. By the various time series time delay and embedding dimension determined respectively in this model, the multi-variable series of excavation deformation for deep foundation pit has been done in the first phase space reconstruction. The neural network input extraction by the use of partial least squares regression method can be the strongest impact components. Finally non-linear fitting between the various components has been completed via the enhanced BP neural network model. With practical application for deformation prediction of deep foundation pit, the method´s effectiveness has been verified. In addition, compared with the traditional BP network, enhanced BP neural network has better convergence and accuracy.
Keywords
backpropagation; deformation; excavators; foundations; least squares approximations; multivariable systems; nonlinear systems; regression analysis; time series; BP neural network model; back propagation neural network model; civil engineering; deep foundation pit; deformation prediction; excavation deformation; impact component; multivariable nonlinear system evolution; multivariate phase space reconstruction; nonlinear fitting; partial least squares regression method; time series time delay; time-varying; Artificial neural networks; Deformable models; Delay; Equations; Mathematical model; Predictive models; Time series analysis; artificial neural network; deformation prediction; multivariable nonlinear system; phase space reconstruction;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location
Yantai
Print_ISBN
978-1-4244-5958-2
Type
conf
DOI
10.1109/ICNC.2010.5582787
Filename
5582787
Link To Document