Approximate b-spline surface based on RBF neural networks

Authors:
Xumin Liu;Houkuan Huang;Weixiang Xu
Affiliations:
School of Computer and Information Technology, Beijing Jiaotong University;School of Computer and Information Technology, Beijing Jiaotong University;School of Traffic and Transportation, Beijing Jiaotong University
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Year:
2005

Citing 4
Cited 0

Self-organization and associative memory: 3rd edition

Self-organization and associative memory: 3rd edition
Original Contribution: On the training of radial basis function classifiers

Neural Networks
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Kernel orthonormalization in radial basis function neural networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Surface reconstruction is a key technology in geometric reverse engineering. In order to get the geometric model of object we need a large number of metrical points to construct the surface. According to the strong points of RBF network such as robust, rehabilitating ability and approximating ability to any nonlinear function in arbitrary precision, we presented a new method to reconstruct B-spline surface by using RBF. Simulation experiments were made based on the theoretical analysis. The result indicated that this model could not only efficiently approximate incomplete surface with noise, automatically delete and repair the input wrong points through self-learning, but has a rapid learning speed, which improves the reconstructing efficiency and precision of dilapidation incomplete surface. The surface obtained by this model has a good smooth character.