recursive b-spline approximation using the kalman filter
Clicks: 165
ID: 233064
2017
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Steady Performance
30.0
/100
164 views
26 readers
Trending
AI Quality Assessment
Not analyzed
Abstract
This paper proposes a novel recursive B-spline approximation (RBA) algorithm which approximates an unbounded number of data points with a B-spline function and achieves lower computational effort compared with previous algorithms. Conventional recursive algorithms based on the Kalman filter (KF) restrict the approximation to a bounded and predefined interval. Conversely RBA includes a novel shift operation that enables to shift estimated B-spline coefficients in the state vector of a KF. This allows to adapt the interval in which the B-spline function can approximate data points during run-time.
| Reference Key |
jauch2017engineeringrecursive
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Jens Jauch;Felix Bleimund;Stephan Rhode;Frank Gauterin |
| Journal | International journal of molecular sciences |
| Year | 2017 |
| DOI |
10.1016/j.jestch.2016.09.015
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.