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Curve Fitting (Power) (G Dataflow)

Version:
    Last Modified: March 15, 2017

    Returns the power fit of a data set using a specific fitting method.

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    y

    Array of dependent values. The length of y must be greater than or equal to the number of unknown parameters.

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    x

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

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    weight

    Weights for the observations.

    weight must be the same size as y. weight also must contain non-zero elements. 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 of weight to 1.

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    tolerance

    Value that determines when to stop the iterative adjustment of the amplitude, power, and offset.

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

    How tolerance Affects the Outputs with Different Fitting Methods

    For the Least Square and Least Absolute Residual methods, if the relative difference between residue in two successive iterations is less than tolerance, this node returns the resulting residue. For the Bisquare method, if any relative difference between amplitude, power, and offset in two successive iterations is less than tolerance, this node returns the resulting amplitude, power, and offset.

    Default: 0.0001

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    error in

    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

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    parameter bounds

    The upper and lower constraints for the amplitude, power, and offset. If you know the exact value of certain parameters, you can set the lower and upper bounds of those parameters equal to the known values.

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    amplitude min

    Lower bound for the amplitude.

    Default: -Infinity, which means no lower bound is imposed on the amplitude.

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    amplitude max

    Upper bound for the amplitude.

    Default: Infinity, which means no upper bound is imposed on the amplitude.

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    power min

    Lower bound for the power.

    Default: -Infinity, which means no lower bound is imposed on the power.

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    power max

    Upper bound for the power.

    Default: Infinity, which means no upper bound is imposed on the power.

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    offset min

    Lower bound for the offset.

    Default: 0

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    offset max

    Upper bound for the offset.

    Default: 0

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    method

    Fitting method.

    Name Value Description
    Least Square 0 Uses the least square method.
    Least Absolute Residual 1 Uses the least absolute residual method.
    Bisquare 2 Uses the bisquare method.

    Algorithm for the Least Square Method

    The least square method of fitting finds the amplitude, power, and offset of the power model by minimizing residue according to the following equation:

    1 N i = 0 N 1 w i ( f i y i ) 2

    where

    • N is the length of y or the number of data values in a waveform
    • wi is the ith element of weight
    • fi is the ith element of best power fit
    • yi is the ith element of y or the ith data value in a waveform

    Algorithm for the Least Absolute Residual Method

    The least absolute residual method finds the amplitude, power, and offset of the power model by minimizing residue according to the following equation:

    1 N i = 0 N 1 w i | f i y i |

    where

    • N is the length of y or the number of data values in a waveform
    • wi is the ith element of weight
    • fi is the ith element of best power fit
    • yi is the ith element of y or the ith data value in a waveform

    Algorithm for the Bisquare Method

    The bisquare method of fitting finds the amplitude, power, and offset of the power model using an iterative process, as shown in the following illustration:

    The node calculates residue according to the following equation:

    1 N i = 0 N 1 w i ( f i y i ) 2

    where

    • N is the length of y or the number of data values in a waveform
    • wi is the ith element of weight
    • fi is the ith element of best power fit
    • yi is the ith element of y or the ith data value in a waveform.

    Default: Least Square

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    best power fit

    Y-values of the fitted model.

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    amplitude

    Amplitude of the fitted model.

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    power

    Power of the fitted model.

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    offset

    Offset of the fitted model.

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    error out

    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.
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    residue

    Weighted mean error of the fitted model.

    If method is Least Absolute Residual, residue is the weighted mean absolute error. Otherwise, residue is the weighted mean square error.

    Algorithm for Calculating best power fit

    This node uses the iterative general least square method and the Levenberg-Marquardt method to fit data to the power function of the general form described by the following equation:

    f = a x b + c

    where

    • x is the input sequence
    • a is amplitude
    • b is power
    • c is offset

    This node finds the values of a, b, and c that best fit the observations (x, y).

    The following equation specifically describes the power function resulting from the general power fit algorithm:

    y [ i ] = a ( x [ i ] ) b + c

    The following illustration shows a power fit result using this node.

    Where This Node Can Run:

    Desktop OS: Windows

    FPGA: This product does not support FPGA devices


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