Particle-based meta-model for continuous breakpoint optimization in smooth local-support curve fitting
作者：
Highlights：
• We compute a smooth meta-model of a given set of data points based on local-support free-form parametric curves.
• Our method applies a particle-based metaheuristic approach to determine optimal values of unknowns of the fitting curve.
• The method does not assume any knowledge about the underlying function of data beyond the data points.
• Our approach performs well and in a fully automatic way even for underlying functions exhibiting challenging features.
• Our experiments show that our approach outperforms previous approaches in terms of generality and fitting error accuracy.
摘要
•We compute a smooth meta-model of a given set of data points based on local-support free-form parametric curves.•Our method applies a particle-based metaheuristic approach to determine optimal values of unknowns of the fitting curve.•The method does not assume any knowledge about the underlying function of data beyond the data points.•Our approach performs well and in a fully automatic way even for underlying functions exhibiting challenging features.•Our experiments show that our approach outperforms previous approaches in terms of generality and fitting error accuracy.
论文关键词：Particle swarm,Meta-model,Breakpoint optimization,Local-support curves,Curve fitting,CAD/CAM
论文评审过程：Received 16 August 2015, Revised 20 October 2015, Accepted 16 November 2015, Available online 22 December 2015, Version of Record 22 December 2015.
论文官网地址：https://doi.org/10.1016/j.amc.2015.11.050
