Nonlinear Least Squares Algorithm

The function LMFsolve.m serves for finding optimal solution of an overdetermined system of nonlinear equations in the least-squares sense. The standard Levenberg- Marquardt algorithm was modified by Fletcher and coded in FORTRAN many years ago. LMFsolve is its essentially shortened version...

The function The LMFnlsq.m serves for finding optimal solution of an overdetermined system of nonlinear equations in the least-squares sense. The standard Levenberg- Marquardt algorithm was modified by Fletcher and coded in FORTRAN many years ago (see the Reference). This version of LMFnlsq is...

The problem of fitting a straight line to data with uncertainties in both coordinates is solved using a weighted total least-squares algorithm. The parameters are transformed from the usual slope/y-axis intersection pair to slope angle and distance to the origin. The advantages of this are that...


Platforms: Matlab

The problem of fitting a straight line to data with uncertainties in both coordinates is solved using a weighted total least-squares algorithm. The parameters are transformed from the usual slope/y-axis intersection pair to slope angle and distance to the origin. The advantages of this are that...


Platforms: Matlab

Constructs discrete least-squares rational approximations to data using the full-Newton algorithm for solving separable non-linear least-squares problems that was developed in:Carlos F. Borges, A Full-Newton Approach to Separable Nonlinear Least Squares Problems and its Application to Discrete...


Platforms: Matlab

This package contains a set of tools that allows to deform in real-time points and images using the Moving Least Squares algorithms. This is a fast technique to get good image deformations without using the computational expansive techniques provided by the thin-plates splines algorithms. The...


Platforms: Matlab

OLS - Orthogonal Least Squares: Proposed by T. Blumensath, M. E. DaviesStOLS - Stagewise OLS: Combining StOMP ideas with OLSROLS - Regularized OLS: Combining ROMP ideas with OLS


Platforms: Matlab

The code implements the algorithm as given in Chen et. al 1991.It takes max tolerance from the user.It selects Ms significant regressors from the total M regressors.Unfortunately, the error obtained after training the RBF network isn't as expected. Is there a mistake in my code???Please feel free...


Platforms: Matlab

We present a Matlab toolbox which can solve basic problems related to the Total Least Squares (TLS) method in the modeling. By illustrative examples we show how to use the TLS method for solution of:- linear regression model- nonlinear regression model- fitting data in 3D space- identification of...


Platforms: Matlab

Like the finite difference method, the Taylor Series Least Squares method can be used to estimate derivatives. The TLS technique can be used to estimate derivatives from scattered or unstructured data. The Hermite Taylor Series Least Squares technique augments the TLS approach with information...


Platforms: Matlab

Very limited application of least squares. It gives you the plotted curves of original and plotted data with the equation in polynomial form. you can also evaluate that polynomial at different points for prediction.note:1.it would be best viewed on 1024x768.2. if you copy and paste data in data...


Platforms: Matlab

ORTHLLS2D returns the Orthogonal Linear Least Squares estimate for parameters of line a x + b y + c = 0function f = OrthLLS2D(x, y)Inputs x and y must be real vectors of equal size.Output f is the real vector [a b c] where a, b and c are the estimated parameters of the linear equation.Since a...


Platforms: Matlab

This function calculates the minimum-norm solution of the least squares problem A*X = B. Where A is low-rank matrix. The function LSMIN is faster then the matlab alternative X = pinv(A)*B. Uses the LAPACK functions (S,C,D,Z)EGLSS or (S,C,D,Z)EGLSD.


Platforms: Matlab

Demonstration of least squares data fitting using both inverse and backslash operators.This example was developed for use in teaching modeling, simulation, and optimization in graduate engineering courses. A corresponding video is available at:


Platforms: Matlab

The DynaFit application was developed to perform nonlinear least-squares regression of chemical kinetic, enzyme kinetic, or ligand-receptor binding data. The experimental data can be either initial reaction velocities in dependence on the concentration of varied species (e.g., inhibitor...


Platforms: Windows

zpkfit uses the nonlinear least-squares fitter (lsqnonlin) to fit an analytic model of any (fixed) number of poles and zeroes to numerical (presumably measured) frequency response data. It takes as arguments a frequency vector, a frequency response vector, and vectors of the initial guesses of...


Platforms: Matlab

This example solves a complex curve fitting problem that involves parameter pooling and stratification using a nonlinear least-squares approach. This example also takes advantage of some new language features with MATLAB 7. * Anonymous functions * Nested functions


Platforms: Matlab

Often, measured data is comprised of N sampled values of z, evaluated at N locations (x,y). With this function, you can calculate the coefficients of the best-fit x,y polynomial using a linear least squares approximation.You can use this function if you have a set of N data triplets x,y,z, and...


Platforms: Matlab

The Kalman filter can be interpreted as a feedback approach to minimize the least equare error. It can be applied to solve a nonlinear least square optimization problem. This function provides a way using the unscented Kalman filter to solve nonlinear least square optimization problems. Three...


Platforms: Matlab

Statistics::LineFit module least squares line fit, weighted or unweighted. SYNOPSIS use Statistics::LineFit; $lineFit = Statistics::LineFit->new(); $lineFit->setData (@xValues, @yValues) or die "Invalid data"; ($intercept, $slope) = $lineFit->coefficients(); defined $intercept or die...


Platforms: *nix