Notice of RetractionAn adaptive SVR modeling method based on VFS for robotic grinding

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.