Notice of RetractionLifetime prediction model of cylinder based on genetic support vector regression

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 order to solve the problem of BP neural network, genetic support vector regression is presented to predict the lifetime of cylinder. Support vector regression (SVR) is a novel prediction algorithm based on structure risk minimization principles, which can lead to great generalization ability. In the genetic support vector regression model, the genetic algorithm is used to optimize the parameters of support vector regression. That´s because that the generalization ability of support vector regression is controlled by its parameters. The wear rate data of 20 mileages are employed to study the lifetime prediction of cylinder by the GSVR model. In order to prove the superiority of GSVR in lifetime prediction of cylinder, the RBF neural network and BP neural network are employed to compare with GSVR. The results of the experiments show that the lifetime prediction model of cylinder by the GSVR is better than that of RBF neural network, BP neural network.