Curve Fitting (Polynomial) (G Dataflow)

Constraints on the polynomial coefficients of a certain order.

Use this input if you know the exact values of certain polynomial coefficients.

This input is available only if you wire one of the following data types to signal or y:

• Waveform
• 1D array of waveforms
• 1D array of double-precision, floating-point numbers

order

Constrained order.

Default: 0

coefficient

Coefficient of the specific order.

Default: 0

Order of the polynomial.

polynomial order must be greater than or equal to 0. If polynomial order is less than zero, this node sets polynomial coefficients to an empty array and returns an error. In real applications, polynomial order is less than 10. If polynomial order is greater than 25, the node sets polynomial coefficients to zero and returns a warning.

Default: 2

A Boolean that specifies whether to reset the internal state of the node.

This input is available only if you wire a double-precision, floating-point number to y or signal.

Default: False

Input signal.

This input accepts a waveform or a 1D array of waveforms.

This input changes to y when the data type is a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.

Dependent values representing the y-values of the data set.

This input accepts a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.

This input changes to signal when the data type is a waveform or a 1D array of waveforms.

Independent values representing the x-values of the data set.

This input accepts a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.

This input is available only if you wire a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers to y or signal.

An array of weights for the data set.

This input is available only if you wire one of the following data types to signal or y:

• Waveform
• 1D array of waveforms
• 1D array of double-precision, floating-point numbers

Value that determines when to stop the iterative adjustment of coefficients when you use the Least Absolute Residual or Bisquare methods.

If tolerance is less than or equal to 0, this node sets tolerance to 0.0001.

This input is available only if you wire one of the following data types to signal or y.

• Waveform
• 1D array of waveforms
• 1D array of double-precision, floating-point numbers

Default: 0.0001

Length of each set of data. The node performs computation for each set of data.

When you set block size to zero, the node calculates a cumulative solution for the input data from the time that you called or initialized the node. When block size is greater than zero, the node calculates the solution for only the newest set of input data.

This input is available only if you wire a double-precision, floating-point number to signal or y.

Default: 100

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.

error in does not contain an error	error in contains an error
If no error occurred before the node runs, the node begins execution normally. If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.	If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Default: No error

Algorithm this node uses to compute the polynomial curve that best fits the input values.

Default: SVD

Method of fitting data to a polynomial curve.

Algorithm for the Bisquare Method

The bisquare method finds the polynomial coefficients using an iterative process, as shown in the following illustration.

The node calculates residue according to the following equation:

$\frac{1}{N}\underset{i=0}{\overset{N-1}{\sum }}{w}_i{(f_i-y_i)}^2$

where

• N is the length of y or the number of data values in a waveform
• w_i is the i^th element of weight
• f_i is the i^th element of best polynomial fit
• y_i is the i^th element of y or the i^th data value in a waveform

Default: Least Square

Polynomial curve that best fits the input signal.

This output can return the following data types:

• Waveform
• 1D array of waveforms
• 1D array of double-precision, floating-point numbers

Coefficients of the fitted model in ascending order of power. The total number of elements in polynomial coefficients is m + 1, where m is polynomial order.

Error information.

The node produces this output 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.

error in does not contain an error	error in contains an error
If no error occurred before the node runs, the node begins execution normally. If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.	If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Weighted mean error of the fitted model.

This output can return a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.