Notice of RetractionLogistics forecasting technology by RBF neural network trained by genetic algorithm

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.

Logistics forecasting is useful to optimize the allocation of resources. Radial basis function(RBF) neural network has strong ability in nonlinear forecasting and high training speed. However, in the radial basis function neural network, the three parameters: the output weights, the centers of radial basis function hidden units and widths of radial basis function hidden units need to be optimized. Then, radial basis function neural network trained by genetic algorithm is applied to logistics forecasting. The logistics forecasting data in 1993~2003 are applied to analyze the superiority of genetic algorithm-RBF neural network. The comparison results between RBF neural network model and RBF neural network trained by genetic algorithm show that genetic algorithm and RBF neural network logistics forecasting model is better than RBF neural network logistics forecasting model.