Notice of RetractionHybrid grey theories and BP algorithm neural network forecast model in logistics park

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.

Hybrid grey theories and BP algorithm neural network forecast model(GBPNN) is established based on the season influence factor from the logistics park side and chose the `trainlm´ to practice from a comparison and analysis of three BP algorithm function which named `traingd, trainlm, thaingdx´. The season influence factor which is used as the GBPNN input layer provided a expert evaluation system to assess cargo flow in the logistics park. Then, as the counts of the network input layers and connotative layers are confirmed by trial calculation, so the local extremum is avoided a lot and the forecasting precision is improved. At lase, by using the distribution data in Gai Jiagou Logistics Park and established the Matlab programming to verify the model, a much better forecasting approach effects and a higher forecasting precision are obtained.