Returns the exponential fit of a data set using a specific fitting method.
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.
Value that determines when to stop the iterative adjustment of the amplitude, damping, and offset.
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.
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, damping, and scale in two successive iterations is less than tolerance, this node returns the resulting amplitude, damping, and scale.
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.
Default: No error
Upper and lower constraints for the amplitude, damping, and offset of the calculated exponential fit.
This input is available only if you wire one of the following data types to signal or y:
Lower bound for the amplitude.
Default: -Infinity, which means no lower bound is imposed on the amplitude.
Upper bound for the amplitude.
Default: Infinity, which means no upper bound is imposed on the amplitude.
Lower bound for the damping.
Default: -Infinity, which means no lower bound is imposed on the damping.
Upper bound for the damping.
Default: Infinity, which means no upper bound is imposed on the damping.
Lower bound for the offset.
Default: 0
Upper bound for the offset.
Default: 0
The fitting method.
This input is available only if you wire one of the following data types to signal or y:
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, damping, and offset of the exponential model by minimizing the residue according to the following equation:
where
Algorithm for the Least Absolute Residual Method
The least absolute residual method finds the amplitude, damping, and offset of the exponential model by minimizing the residue according to the following equation:
where
Algorithm for the Bisquare Method
The bisquare method of fitting finds the amplitude, damping, and offset using an iterative process, as shown in the following illustration.
The node calculates residue according to the following equation:
where
Default: Least Square
Amplitude of the fitted model.
This output can return a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.
Damping of the fitted model.
This output can return a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.
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.
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.
Algorithm for Calculating residue When the Input Signal is a Double-Precision, Floating-Point Number
When the input signal is a double-precision, floating-point number, this node calculates residue according to the following equation:
where
This node uses the iterative general least square method and the Levenberg-Marquardt method to fit data to an exponential curve of the general form described by the following equation:
where
This node finds the values of a, b, and c that best fit the observations (x, y).
The following equation specifically describes the exponential curve resulting from the exponential fit algorithm:
Where This Node Can Run:
Desktop OS: Windows
FPGA: Not supported
Web Server: Not supported in VIs that run in a web application