Direct spline interpolation of noisy data may result in a curve with
unwanted oscillations. This is particularly bad if the slope of the
curve is important.
A better approach is to reduce the degrees of freedom for the spline
and use the method of least squares to fit the spline to the noisy
data. The deegres of freedom are connected to the number of breaks
(knots), so the smoothing effect is controlled by the selection of
- A curve fitting tool based on B-splines
- Splines on ppform (piecewise polynomial)
- Any spline order (cubic splines by default)
- Periodic boundary conditions
- Linear constraints on function values and derivatives
- Robust fitting scheme
- Operates on ND arrays in the same way as SPLINE
- Nonuniform distributions of breaks
M-FILES ALSO INCLUDED:
examples - Examples for splinefit
ppdiff - Differentiate piecewise polynomial
ppint - Integrate piecewise polynomial
Jonas Lundgren (2019). SPLINEFIT (https://www.mathworks.com/matlabcentral/fileexchange/71225-splinefit), MATLAB Central File Exchange. Retrieved .
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