Exponential Fit PtByPt VI

Owning Palette: Fitting PtByPt VIs

Requires: Full Development System

Finds the exponential curve values and the exponential coefficients amplitude and damping, using the least-squares solution, which describe the exponential curve that best represents the input set of data points defined by sample length.

This VI is similar to the Exponential Fit VI.

Note  By default, reentrant execution is enabled in all Point By Point VIs.

Details  

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	initialize, when TRUE, initializes the internal state of the VI.
	y is an input data point.
	x is an input data point.
	sample length is the length of each set of incoming data. The VI performs computation for each set of data. The default is 100. When you set sample length to zero, the VI calculates a cumulative solution for the input data from the time that you called or initialized the VI. When the sample length setting is greater than zero, the VI calculates the solution for only the newest set of input data.
	Best Exponential Fit returns the y-values of the fitted model.
	amplitude is the exponential curve amplitude value that best describes the curve. Note  You must call this VI at least twice to obtain a valid result for amplitude.
	damping is the exponential curve damping value that best describes the curve. Note  You must call this VI at least twice to obtain a valid result for damping.
	mse is the mean square error.
	error returns any error or warning from the VI. You can wire error to the Error Cluster From Error Code VI to convert the error code or warning into an error cluster.

Exponential Fit PtByPt Details

The general form of the exponential fit is given by

F = a*exp(d*X),

where

F is the set of output data Best Exponential Fit.

X is x.

a is amplitude.

d is damping.

The mean square error (MSE) between the Best Exponential Fit and the set of input data yi is determined using the MSE PtByPt VI and returned in the output mse. mse = 1/n* sum[f(i)-y(i)]². Here n is the number of elements in the set of input data.

Note  The Exponential Fit PtByPt VI performs an exponential fit even when the set of y-values is negative. The Exponential Fit PtByPt VI performs the fit under the assumption that the amplitude coefficient is also negative and returns a negative amplitude. The set of y-values cannot contain both positive and negative elements.