Support Vector Machine Prediction Modeling for Automobile Ownership

To address some inherent defects of artificial neural networks, such as insufficient generalization performance, local extremum problem, and dimensional catastrophe problem, a support vector machine was proposed and applied to the modeling of automobile ownership prediction. By analyzing the data on automobile ownership and its influencing factors, the learning sample couples for automobile ownership prediction modeling were constructed, and support vector machine (SVM) was used to regression the nonlinear function relation of automobile ownership prediction model, and the established automobile ownership prediction model was used to predict the automobile ownership in different years. To reduce the impact on the accuracy of automobile ownership prediction caused by the large order of magnitude difference between the data of automobile ownership and its influence factors, the normalization method was used to pre-process the automobile ownership and its influence factors, and the inverse normalization was used to process the automobile ownership prediction results. The comparison between the automobile ownership prediction results and the statistical results shows that the automobile ownership prediction model has good generalization performance, and the support vector machine is an effective method to model the automobile ownership prediction.

A new grey predictionmodel for forecasting the automobiles ownership in China

The average relative simulation and prediction percentage errors of the new model are only 0.092%and 3.023%,respectively.The simulation and prediction errors obtained from the classical GM(1,1)and the DGM(1,1)models are,respectively,2.064%and 6.980%in the first case,and 1.942%and 7.360%in the second.The findings show that the GM(1,1,4)model has the best performance,which confirms the effectiveness of the structure improvement.The new model can enhance the smoothness of the background value and weaken the effects of extreme values in the raw sequence in the model’s performance.Therefore,the simulation and prediction performances of the GM(1,1,4)model are better than those of the traditional grey prediction models.The prediction show that the ownership for automobiles in China will grow rapidly in future.Findings could help the government in formulating adjustments to the industrial structures,and facilitate making rational yield plans for automobile firms.

The Status, Problems and Countermeasures of the Auto Industry on Utilizing FDI in China

The purpose of this paper is to analyze the status of the automobile industry on utilizing FDI in China. So firstly this paper has introduced the scale of auto industry absorbing FDI, and the stock ownership structure of the joint ventures. Then on this basis, the paper has analyzed the positive and negative influence that FDI brings to the auto industry. Finally some countermeasures have been put forward.