| Popis: |
This paper studies variable selection problems for high dimensional nonlinear nonparametric systems. Based on directional regression, one new variable selection approach is proposed, which is not affected by the curse of dimensionality that traditional variable selection approaches usually exist. In addition, since indicators for redundant variables aren’t exact zero’s, it is difficult to decide variables whether are redundant or not when the indicators are small. This is critical in the variable selection problem because the variable is either selected or unselected. To solve this problem, a penalty optimization algorithm is proposed to ensure the convergence of the set. Simulation and experimental research verify the effectiveness of the variable selection method proposed in this paper. |
