DocumentCode
1932188
Title
Notice of Retraction
An adaptive SVR modeling method based on VFS for robotic grinding
Author
Yang Yang ; Yixu Song ; Jiaxin Wang ; Zhongxue Gan ; Lizhe Qi
Author_Institution
Dept. of Comput. S&T, Tsinghua Univ., Beijing, China
Volume
8
fYear
2010
fDate
9-11 July 2010
Firstpage
438
Lastpage
442
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 performance of a model, which is trained with offline data, is highly relied on the conditions in which the system is working. When the working conditions change, the prediction accuracy of the model will be reduced significantly. To solve this problem, we propose an adaptive SVR modeling method based on vector-field-smoothed (VFS) algorithm. This method can adapt the model quickly to new working conditions by using only a few adaptive samples. Also, it can extend the feature subspace which the model covers so as to enhance the generalization ability of the model. The experimental results show that the model using this method can achieve a much better performance than the original model, as well as the model using other adaptive SVR modeling method.
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 performance of a model, which is trained with offline data, is highly relied on the conditions in which the system is working. When the working conditions change, the prediction accuracy of the model will be reduced significantly. To solve this problem, we propose an adaptive SVR modeling method based on vector-field-smoothed (VFS) algorithm. This method can adapt the model quickly to new working conditions by using only a few adaptive samples. Also, it can extend the feature subspace which the model covers so as to enhance the generalization ability of the model. The experimental results show that the model using this method can achieve a much better performance than the original model, as well as the model using other adaptive SVR modeling method.
Keywords
grinding; industrial robots; regression analysis; support vector machines; vectors; VFS; adaptive SVR modeling method; robotic grinding; support vector regression; vector-field-smoothed algorithm; Adaptation model; Laser modes; Predictive models; Robots; SVR; VFS; adaptation; modeling; robotic grinding;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5563755
Filename
5563755
Link To Document