The pwpfit Toolbox for Polynomial and Piece-wise Polynomial Data Fitting

被引：3
|
作者：
Cunis, Torbjorn [1 ,2 ]
机构：
[1] Off Natl Etud & Rech Aerosp, Dept Informat Proc & Syst, Ctr Midipyrenees, F-31055 Toulouse, France
[2] French Sch Civil Aviat, Drones Res Grp, F-31055 Toulouse, France
来源：
IFAC PAPERSONLINE | 2018年 / 51卷 / 15期
关键词：
Grey Box Modeling; Toolboxes; Mechanical and Aerospace; Multivariable System Identification; Nonlinear System Identification; Hybrid System Identification; REGRESSION; POINTS;
D O I：
10.1016/j.ifacol.2018.09.204
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Several techniques have been proposed for piece-wise regression as extension to standard polynomial data fitting, either selecting the joints a priori or adding computational load for optimal joints. The pwpfit (1) toolbox provides piece-wise polynomial fitting without pre-selection of joints using linear-least square (LSQ) optimization only. Additional constraints are realised as constraint matrices for the LSQ problem. We give an application example for the multi-variable aerodynamic coefficients of the general transport model in pre-stall and post-stall. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：682 / 687
页数：6
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