Levenberg Marquardt (Not in Base Package)

Uses the Levenberg-Marquardt algorithm to determine the least squares set of coefficients that best fit the set of input data points (X,Y) as expressed by a nonlinear function y=f(x,a) where a is the set of coefficients. Details

Standard Deviation is the array of standard deviations, sigma[i] for data point (x[i],y[i]). Internally, LabVIEW sets all data points to 1.0.
X is the input array representing the independent variable x. The number of valid input points must be greater than zero and greater than the number of specified coefficients. The number of elements in X must be equal to the number of elements in Y.
Y is the array of data points representing the observed values of the dependent variable y. The number of valid input points must be greater than zero and greater than the number of specified coefficients. The number of elements in Y must be equal to the number of elements in X.
Initial Guess Coefficient denotes your initial-guessed solution. Using this function successfully depends on how close your initial guess coefficients are to the solution. Therefore, it is always worth taking the time and effort to obtain good initial guess coefficients to the solution from any available resources before using the function.
max iteration is the maximum executing iteration. If the VI reaches maximum iteration without finding a solution, the function returns an error. You must increase the max iteration or adjust the Initial Guess Coefficient to get a solution. The default is 200.
model description is a cluster containing the fitting equation.
model is a string describing the model equation.
Parameters is an array of strings of the unknown parameters.
x is a string describing the independent variable.
Covariance is the matrix of covariances. c[j,k] is the covariance between a[j] and a[k], and c[j,j] is the variance of a[j].
Best Fit Coefficients is the array of coefficients that minimize the chi-square error between the solution vector and the observed Y values. The chi-squared function is given by

In this equation, are the input data points, and

is the nonlinear function where are coefficients. If the measurement errors are independent and normally distributed with constant, standard deviation this is also the least-square estimation.

Best Fit is the fitted data z[i] = f(x[i],A), where A is the set of Best Fit Coefficients.
mse is the mean squared error.
ticks is the time in milliseconds for the whole calculation.
error returns any error or warning condition from the VI.

Levenberg Marquardt Details

The VI assumes that you have prior knowledge of the nonlinear relationship between the x and y coordinates. That is,

f = f(X, A)

where the set of coefficients, A, is determined by the Levenberg-Marquardt algorithm.