Surface Reconstruction Based on Hybrid Training Method for RBFNN
基于混合训练方法的RBF神经网络的曲面重构

Based on RBFNN's capabilities of approaching a no-linear function,powerful anti-noising,and repairing and so on,this paper describes the present training methods of RBFNN.The proposed method applies the RBFNN to the free surface reconstruction from an unorganized cloud of points in which always involve noise.Furthermore,it also discusses the proposed method's feasibility in theory and validates its practicability.The results show that the reconstruction method using RBFNN can not only approach the incomplete surface with noise effectively,but also have a high fitting precision and a high net-training speed.The above advantages indicate the feasibility of applying RBFNN to surface reconstruction.RBFNN provides a new method for RE's key technology:free surface recons.