Version:

Last Modified: June 25, 2019

Uses B-spline fitting to smooth a data set. The input data must be two arrays.

Number of control points that fit to the data set.

**number of control points** must be greater than **degree**.

**Default: **10

Dependent values. **y** must contain at least two points.

Independent values. **x** must be the same size as **y**.

Weights for the observations.

**weight** must be the same size as **y**. The elements in **weight** cannot be 0. If an element in **weight** is less than 0, this node uses the absolute value of the element. If you do not wire an input to **weight**, this node sets all elements in **weight** to 1.

Error conditions that occur before this node runs.

The node responds to this input according to standard error behavior.

Standard Error Behavior

Many nodes provide an **error in** input and an **error out** output so that the node can respond to and communicate errors that occur while code is running. The value of **error in** specifies whether an error occurred before the node runs. Most nodes respond to values of **error in** in a standard, predictable way.

**Default: **No error

Order of polynomials that form the B-spline curve and fit to the data set.

**Default: **3

Method that computes the interim knot vector.

Name | Value | Description |
---|---|---|

equally spaced | 0 | Uses the equally-spaced method. |

chord length | 1 | Uses the chord length method. |

centripetal | 2 | Uses the centripetal method. |

**Default: **centripetal

Y-values of the B-Spline curve that best fit the input data set.

X-values of the B-Spline curve that best fit the input data set.

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

**error in** input and an **error out** output so that the node can respond to and communicate errors that occur while code is running. The value of **error in** specifies whether an error occurred before the node runs. Most nodes respond to values of **error in** in a standard, predictable way.

Weighted mean square error of the fitted model.

This node calculates **best B-Spline fit x** and **best B-Spline fit y** by minimizing the **residue** according to the following equation:

$\frac{1}{N}\underset{i=0}{\overset{N-1}{\sum}}{w}_{i}\cdot {\Vert ({x}_{i},{y}_{i})-({x\prime}_{i},{y\prime}_{i})\Vert}^{2}=\frac{1}{N}\underset{i=0}{\overset{N-1}{\sum}}{w}_{i}\cdot [{({x}_{i}-{x\prime}_{i})}^{2}+{({y}_{i}-{y\prime}_{i})}^{2}]$

where

*N*is the length of**y***w*_{i}is the*i*^{th}element of**weight**- (
*x*_{i},*y*_{i}) is the*i*^{th}pair of the input sequences (**x**,**y**) - (x'
_{i}, y'_{i}) is the*i*^{th}pair of (**best B-Spline fit x**,**best B-Spline fit y**) - The norm symbols (||) on both sides of the function compute the |
_{2}norm of a vector

The standard B-Spline basis functions construct the B-Spline curve (x'_{i}, y'_{i}).

**Where This Node Can Run: **

Desktop OS: Windows

FPGA: Not supported

Web Server: Not supported in VIs that run in a web application