The NURBS book
Advanced surface fitting techniques
Computer Aided Geometric Design
Extending Neural Networks for B-Spline Surface Reconstruction
ICCS '02 Proceedings of the International Conference on Computational Science-Part II
A New Artificial Intelligence Paradigm for Computer-Aided Geometric Design
AISC '00 Revised Papers from the International Conference on Artificial Intelligence and Symbolic Computation
Automatic Knot Placement by a Genetic Algorithm for Data Fitting with a Spline
SMI '99 Proceedings of the International Conference on Shape Modeling and Applications
Capturing Outline of Fonts Using Genetic Algorithm and Splines
IV '01 Proceedings of the Fifth International Conference on Information Visualisation
Functional networks for B-spline surface reconstruction
Future Generation Computer Systems
Computing optimized curves with NURBS using evolutionary intelligence
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and its Applications - Volume Part I
Developable surface modelling by neural network
Mathematical and Computer Modelling: An International Journal
Information Sciences: an International Journal
Hi-index | 0.00 |
In surface fitting problems, the selection of knots in order to get an optimized surface for a shape design is well-known. For large data, this problem needs to be dealt with optimization algorithms avoiding possible local optima and at the same time getting to the desired solution in an iterative fashion. Many computational intelligence optimization techniques like evolutionary optimization algorithms, artificial neural networks and fuzzy logic have already been successfully applied to the problem. This paper presents an application of another computational intelligence technique known as "Artificial Immune Systems (AIS)" to the surface fitting problem based on B-Splines. Our method can determine appropriate number and locations of knots automatically and simultaneously. Numerical examples are given to show the effectiveness of our method. Additionally, a comparison between the proposed method and genetic algorithm is presented.