.png){kind=icon}
.png){kind=icon}
Return
Positive data modeling using spline function
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2010.03.034.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A rational cubic function with two parameters has been constructed to visualize the positive data. The main focus of the work is the representation of the data in such a way that its view looks smooth and attractive. In the first step simple data dependent constraints are derived on the parameters in the description of the rational cubic function to visualize the shape of positive data then, it is extended to a rational bi-cubic partially blended functions (Coons-patches) and derived constraints on parameters to visualize the shape of positive surface data. The developed scheme is locally positive and economical. The approximation order of rational cubic spline function is O(h(i)(3)). (C) 2010 Elsevier Inc. All rights reserved.
Keywords:
Spline
Shape preservation
Interpolation
Positivity
Data
Journal
IF:
3.4
Papers:
2.3W
Citations:
3.3W