Using rational B-spline neural networks for curve approximation

Authors:
Tang Van To;Tanawat Kositviwat
Affiliations:
Department of Computer Science, Faculty of Science and Technology, Assumption University, Bangkok, Thailand;Department of Computer Science, Faculty of Science and Technology, Assumption University, Bangkok, Thailand
Venue:
MMACTE'05 Proceedings of the 7th WSEAS International Conference on Mathematical Methods and Computational Techniques In Electrical Engineering
Year:
2005

Citing 7
Cited 0

Projective geometry and its applications to computer graphics

Projective geometry and its applications to computer graphics
Modifying the shape of rational B-splines. part 1: curves

Computer-Aided Design
Curves and surfaces for computer aided geometric design (3rd ed.): a practical guide

Curves and surfaces for computer aided geometric design (3rd ed.): a practical guide
Curve and surface fitting with splines

Curve and surface fitting with splines
Neurofuzzy adaptive modelling and control

Neurofuzzy adaptive modelling and control
Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
On NURBS: a Survey

IEEE Computer Graphics and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Rational B-spline neural network (RBNN) is a neural network can be used for curves and surfaces approximation using rational B-spline model. The approximation is solved by learning process of rational B-spline neural networks from observation data points. A hybrid genetic based algorithm for optimizing knots, control points and weights of RBNN.