Exponential Fit (Not in Base Package)

Finds the exponential curve values and the set of exponential coefficients amplitude and damping, which describe the exponential curve that best represents the input data set. Details

Y Values must have the same sign and must contain at least two points. That is, >0 for i=0, 1,...n – 1 or <0 for i=0, 1,...n – 1, n 2, where Y represents the input sequence Y Values and n is the number of data points. If the signs are inconsistent or there are less than two sample points, the VI sets Best Exponential Fit to an empty array, sets amplitude, damping, and mse to NaN, and returns an error via the Exponential Fit Coefficients VI. Refer to Exponential Fit Coefficients for more information about this VI.
X Values must contain at least two points. n is greater than or equal to 2, where n is the number of data points.
Best Exponential Fit is the calculated values of the best exponential fit.
amplitude is the amplitude of Best Exponential Fit.
damping is the damping constant of Best Exponential Fit.
mse is the mean squared error.
error returns any error or warning condition from the VI.

Exponential Fit Details

The general form of the exponential fit is given by

where F is the output sequence Best Exponential Fit, X is the input sequence X Values, a is the amplitude, and is the damping constant.

The VI obtains mse using the formula

where f is the output sequence Best Exponential Fit, y is the input sequence Y Values, and n is the number of data points.

Note  This VI performs an exponential fit even when the elements of Y Values are negative. It performs the fit under the assumption that the amplitude coefficient is also negative and returns a negative amplitude. Y Values cannot contain both positive and negative elements.