4 edition of **Nonlinear least-squares inversion of infinite line source data (Program NLSINF)** found in the catalog.

Nonlinear least-squares inversion of infinite line source data (Program NLSINF)

- 231 Want to read
- 35 Currently reading

Published
**1989**
by U.S. Dept. of the Interior, Geological Survey, [Books and Open-File Reports Section, distributor] in Denver, Colo
.

Written in English

- Geomagnetism -- Computer programs,
- Magnetotelluric prospecting -- Computer programs

**Edition Notes**

Includes bibliographical references (leaf 15)

Other titles | Nonlinear least squares inversion of infinite line source data .. |

Statement | by Walter L. Anderson |

Series | Open-file report -- 89-555, U.S. Geological Survey open-file report -- 89-555 |

Contributions | Geological Survey (U.S.) |

The Physical Object | |
---|---|

Format | Microform |

Pagination | 1 microfiche |

ID Numbers | |

Open Library | OL13565927M |

The nonlinear parameters of the composite landing gear were identified by (15) with the nonlinear least squares method based on the results of the nonlinear static test. A Numerical-Experimental Method for Drop Impact Analysis of Composite Landing Gear. NL2SOL is a modular program for solving the nonlinear least-squares problem that incorporates a number of novel features. It maintains a secant approximation S to the second-order part of the.

Details. An nls object is a type of fitted model object. It has methods for the generic functions anova, coef, confint, deviance, veterans-opex.comal, fitted, formula, logLik, predict, print, profile, residuals, summary, vcov and weights.. Variables in formula are looked for first in data, then the environment of formula and finally along the search path. Functions in formula are searched for first in. SIAM Journal on Numerical Analysis , () The variable projection method for waveform inversion with an unknown source function. SEG Technical Program Expanded Abstracts , Separable nonlinear least-squares methods for on-line estimation of neural nets Hammerstein veterans-opex.com by:

Least-squares problems fall into two categories: linear or ordinary least squares and nonlinear least squares, depending on whether or not the residuals are linear in all unknowns. The linear least-squares problem occurs in statistical regression analysis; it has a closed-form solution. The nonlinear problem is usually solved by iterative. Dec 16, · The nonlinear least squares problem with linear bound constraints is very similar to the nonlinear least squares problem without constraints but it has the following constraints: See usage examples in the examples\solverf\source folderof your Intel® MKL .

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Part or all of this report is presented in Portable Document Format (PDF). For best results viewing and printing PDF documents, it is recommended that you download the documents to your computer and open them with Adobe veterans-opex.com: W.L. Anderson. Get this from a library. Nonlinear least-squares inversion of infinite line source data (Program NLSINF).

[Walter L Anderson; Geological Survey (U.S.)]. Nonlinear Least Squares Data Fitting D.1 Introduction A nonlinear least squares problem is an unconstrained minimization problem of the form minimize x f(x)= m i=1 f i(x)2, where the objective function is deﬁned in terms of auxiliary functions {f i}.It is called “least squares” because we are minimizing the sum of squares of these functions.

Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters (m ≥ n).It is used in some forms of nonlinear veterans-opex.com basis of the method is to approximate the model by a linear one and to refine the parameters by successive iterations.

MINPACK is a C++ library which solves systems of nonlinear equations, or carries out the least squares minimization of the residual of a set of linear or nonlinear equations, by Jorge More, Danny Sorenson, Burton Garbow, Kenneth Hillstrom.

MINPACK includes software for solving nonlinear equations and nonlinear least squares problems. This book provides an introduction into the least squares resolution of nonlinear inverse problems. The first goal is to develop a geometrical theory to analyze nonlinear least square (NLS) problems with respect to their quadratic wellposedness, i.e.

both wellposedness and optimizability. The Levenberg-Marquardt and trust-region-reflective methods are based on the nonlinear least-squares algorithms also used in fsolve.

The default trust-region-reflective algorithm is a subspace trust-region method and is based on the interior-reflective Newton method described in [1] and [2].cgiterations: Total number of PCG iterations (trust-region-reflective, algorithm only).

Optimization and Data Fitting { Nonlinear Least-Squares Problems 2 Non-linearity A parameter α of the function f appears nonlinearly if the derivative ∂f/∂α is a function of α. The model M (x,t) is nonlinear if at least one of the parameters in x appear nonlinearly.

For example, in the exponential decay model. Nonlinear regression. Stata’s nl fits an arbitrary function by least squares.

That is, given y j = f(x j, b) + u j. nl finds b to minimize Σ j (u j 2).(nl can also obtain weighted least squares estimates.)You need only supply the function f(); you do not need to supply the derivatives.

nl provides three ways to define the function. Most commonly, you simply type the function directly on the. Mar 12, · In this screencast, we will look at an example of the mechanics behind non-linear least squares.

The example focuses on fitting the Dorsal gradient in fly embryos to a bell-shaped curve. The Differentiation of Pseudo-Inverses and Nonlinear Least Squares Problems Whose Variables Separate.

Related Databases Efficient scattering-angle enrichment for a nonlinear inversion of the background and perturbations components of a velocity model.

An analysis of separable least squares data driven local coordinates for maximum Cited by: Given measured data, we establish a relationship between independent and dependent variables so that we can use the data predictively. The main concern of Least Squares Data Fitting with Applications is how to do this on a computer with efficient and robust computational methods for Cited by: Least Squares Adjustment: Linear and Nonlinear Weighted Regression Analysis 2 Nonlinear Least Squares 26 Judged visually the line seems to model the data fairly well.

[end of example] More generally let us consider n observations of one dependent (or response). Eight Examples of Linear and Nonlinear Least Squares CEEME — System Identiﬁcation — Fall, c Henri P. Gavin, September 25, – The best fit line represents the best fit for the transformed variables, not the original variables • Another method is to perform nonlinear regression to directly determine the least-squares fit, e.g., – Using the MATLAB fminsearch function NM – Berlin Chen 13 n i a x f a a y 1 y i a e 2 0, 1 [ 0 (1 1)] y a.

Linear or Nonlinear Least-Squares Analysis of Kinetic Data. Charles L. Perrin* Department of Chemistry, University of California San Diego, La Jolla, CaliforniaUnited States *S Supporting Information ABSTRACT: The disadvantages of the usual linear least-squares analysis of ﬁrst- and second-order kinetic data are described,Cited by: 6.

Details. An nls object is a type of fitted model object. It has methods for the generic functions anova, coef, confint, deviance, veterans-opex.comal, fitted, formula, logLik, predict, print, profile, residuals, summary, vcov and weights.

Variables in formula (and weights if not missing) are looked for first in data, then the environment of formula and finally along the search path. Nonlinear Least Squares With and Without Jacobian.

Example showing the use of analytic derivatives in nonlinear least squares. Nonlinear Curve Fitting with lsqcurvefit. Example showing how to do nonlinear data-fitting with lsqcurvefit. Fit an Ordinary Differential Equation (ODE).

For Nonlinear Least Squares Fitting to a number of unknown parameters, linear least squares fitting may be applied iteratively to a linearized form of the function until convergence is achieved. Depending on the type of fit and initial parameters chosen, the nonlinear fit may have good or poor convergence properties.

2 Chapter 5. Least Squares The symbol ≈ stands for “is approximately equal to.” We are more precise about this in the next section, but our emphasis is on least squares approximation. The basis functions ϕj(t) can be nonlinear functions of t, but the unknown parameters, βj, appear in the model veterans-opex.com system of linear equations.

We describe an intuitive and rapid procedure for analyzing experimental data by nonlinear least-squares fitting (nlsF) in the most widely used spreadsheet program. experimental data in x/y form and data calculated from a regression equation are inputted and.Generalized Nonlinear Inverse Problems Solved Using the Least Squares Criterion Albert Tarantola and Bernard Valette Institut de Physique du Globe de Paris, Paris, France Reviews of Geophysics and Space Physics, Vol.

20, No. 2, pages –, May We attempt to give a general deﬁnition of the nonlinear least squares inverse problem.Teunissen [31,32,33, 34] researched the parameter estimation of nonlinear least squares, and the effects of nonlinearity on the parameters and covariance were discussed, with the approximate.