Notice of RetractionForecasting the natural forest stand age based on artificial neural network model

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.

Stand age is the average age of trees in the stand. In this paper, the back propagation (BP) artificial neural network, the projection pursuit regression (PPR) artificial neural network and the multiple stepwise regression anatomic models were introduced to predict the nonlinear relation between the natural stand age and the stand factors. At the same time, the author contrasted the forecasting results precision of the BP artificial neural network model, the PPR artificial neural network and the multiple stepwise regression anatomic models. The result indicated that 3 models were available for prediction of natural forest age; the predication average relative error of BP artificial neural network model was 0.1, he predication average relative error of multiple stepwise regression anatomic models was 0.05, the predication average relative error of PPR artificial neural network model was 0.02; the stability of BP artificial neural network model was poor and in other hand the PPR model and the multiple stepwise regression anatomic model with good stability. Therefore, the PPR model was better than the other two models, which can be applied to predict the natural forest stand age.