least squares npl plural noun: Noun always used in plural form--for example, jeans, scissors. (method of quantity estimation) (Mathématiques) méthode des moindres carrés nf nom féminin: s'utilise avec les articles la, l' (devant une voyelle ou un h muet), une. Ex : fille - nf > On dira la fille ou une fille. Avec un nom féminin, l'adjectif s'accorde. En général, on ajoute. Nonlinear Least Squares. Curve Fitting Toolbox software uses the nonlinear least-squares formulation to fit a nonlinear model to data. A nonlinear model is defined as an equation that is nonlinear in the coefficients, or a combination of linear and nonlinear in the coefficients. For example, Gaussians, ratios of polynomials, and power functions. The formulas for linear least squares fitting were independently derived by Gauss and Legendre. 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. However, it is often also possible to linearize a nonlinear function at the outset and still use linear.

The least squares method is a statistical technique to determine the line of best fit for a model, specified by an equation with certain parameters to observed data LeastSquares [ m, b] finds an x that solves the linear least-squares problem for the matrix equation m. x== b Ordinary least squares fails to consider uncertainty in the operator, modeling all noise in the observed signal. Total least squares accounts for uncertainty in the data matrix, but necessarily increases the condition number of the operator compared to ordinary least squares. Tikhonov regularization or ridge regression is frequently employed to. GitHub is where people build software. More than 50 million people use GitHub to discover, fork, and contribute to over 100 million projects

least squares - English-French Dictionary WordReference

Least-Squares Fitting - MATLAB & Simulink - MathWorks Franc

Least-squares minimization applied to a curve-fitting problem. Notes. Method 'lm' (Levenberg-Marquardt) calls a wrapper over least-squares algorithms implemented in MINPACK (lmder, lmdif). It runs the Levenberg-Marquardt algorithm formulated as a trust-region type algorithm. The implementation is based on paper , it is very robust and efficient with a lot of smart tricks. It should be your. Constrained Least-Squares Multiwell Deconvolution. A key issue in any deconvolution, single-well or multiwell, is the lack of a unique solution, which results in sets or ranges of possible response functions that yield pressure matches of indistinguishable quality. When this space of possible solutions is large, then the associated response function could correspond to reservoir models with. Traductions en contexte de a least-squares en anglais-français avec Reverso Context : a least-squares estimation matrix from the transmitted pilot symbols; formin

Least Squares Fitting -- from Wolfram MathWorl

  1. Traductions en contexte de least-squares weighted en anglais-français avec Reverso Context : The intensities of 8153 independent reflections were measured on a four circle diffractometer, of which 3701 were used in the refinement of the structure to give a final least-squares weighted residue of 0.053
  2. Classic methods try to solve the IK problem by optimizing a cost function, usually a least-squares or augmented least-squares criterion (Söderkvist and Wedin, 1993, De Groote et al., 2008). Other methods that utilize artificial neural nets ( Duka, 2014 ), heuristic algorithms ( Aristidou and Lasenby, 2011 ) and sequential Monte Carlo ( Courty and Arnaud, 2008 ) have been used as well.
  3. Traduction de 'least squares' dans le dictionnaire anglais-français gratuit et beaucoup d'autres traductions françaises dans le dictionnaire bab.la
  4. Visualisation of various optimisation methods for linear regression, like lasso, ridge and elasticnet
  5. ing the curve that best describes the relationship between expected and observed sets of data by
  6. Traductions de expression LEAST SQUARES du anglais vers français et exemples d'utilisation de LEAST SQUARES dans une phrase avec leurs traductions: Kalman filter least squares kalman filter
  7. De très nombreux exemples de phrases traduites contenant least-squares function - Dictionnaire français-anglais et moteur de recherche de traductions françaises

Video: Least Squares Method Definition - investopedia

Nonlinear least-squares solver. Norm of First-order Iteration Func-count f(x) step optimality 0 3 359677 2.88e+04 Objective function returned Inf; trying a new point.. Retrieve the evaporation duct height by least-squares support vector machine algorithm. Journal of applied remote sensing , Bellingham, WA : SPIE, 2009, pp.033503. 10.1117/1.3081546 Nonlinear Least Squares Fitting. Given a function of a variable tabulated at values , assume the function is of known analytic form depending on parameters , and consider the overdetermined set of equation De très nombreux exemples de phrases traduites contenant geometric least-squares - Dictionnaire français-anglais et moteur de recherche de traductions françaises

Vérifiez les traductions'least-squares method' en Français. Cherchez des exemples de traductions least-squares method dans des phrases, écoutez à la prononciation et apprenez la grammaire The least-squares criterion is a method of measuring the accuracy of a line in depicting the data that was used to generate it. That is, the formula determines the line of best fit The method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems (sets of equations in which there are more equations than unknowns) by minimizing the sum of the squares of the residuals made in the results of every single equation.. The most important application is in data fitting.The best fit in the least-squares sense minimizes.

LeastSquares—Wolfram Language Documentatio

Least-squares¶ In a least-squares, or linear regression, problem, we have measurements \(A \in \mathcal{R}^{m \times n}\) and \(b \in \mathcal{R}^m\) and seek a vector \(x \in \mathcal{R}^{n}\) such that \(Ax\) is close to \(b\). Closeness is defined as the sum of the squared differences 14.3 Model Function. The model function is used by the least squares engine to evaluate the model components model i given some test parameters p k.It is therefore a multivariate function (it depends on the various p k) and it is vector-valued (it has several components model i).There must be exactly one component model i for each target (or observed) component target i, otherwise some.

Robust least squares for quantized data matrices

  1. g and related technical career opportunities; Talent Hire technical talent; Advertising Reach developers worldwid
  2. Least squares approximation of a discrete function F(x) with orthogonal polynomials Program to demonstrate Chi-square Statistic Program to demonstrate one dimensional operation of the multi-nonlinear regression Program to demonstrate least squares polynomial fitting Explanation File of Program above (Lsqply) NE
  3. Least square means are means for groups that are adjusted for means of other factors in the model. Imagine a case where you are measuring the height of 7th-grade students in two classrooms, and want to see if there is a difference between the two classrooms
  4. Le module Modèles Généraux PLS est un outil complet pour réaliser et étudier des régressions PLS (Partial Least Squares). Cette technique permet l'analyse des relations entre un nombre important de prédicteurs de différents types et un nombre important de variables dépendantes (réponses). Les modèles peuvent inclure des effets à un seul degré de liberté pour des variables.
  5. ing the curve that best describes the relationship between expected and observed sets of data by
  6. ing the best fit for the data. When you have only one independent x-variable, the calculations for m and b are based on the following formulas: where x and y are sample means; that is, x = AVERAGE(known x's) and y = AVERAGE(known_y's). The line- and curve-fitting functions LINEST and LOGEST can calculate the best straight line or exponential.
  7. Example : Least squares estimation using backcasting procedure (cont'd) Estimate the values of t using a forward recursion ˆ t = ˆu t −θ (0) 1 ˜ t−1 −···−θ (0) q ˜ t−q Minimize the sum of squared residuals using the fitted values ˆ t: XT q+1 (X t − ˆ t −θ 1ˆ t−1 −···−θ qˆ t−q) 2 and find δˆ(1) = θ(1) 1,··· ,θ (1) q 0. Repeat the backcast step.

least-squares · GitHub Topics · GitHu

La régression des moindres carrés partiels a été inventée en 1983 par Svante Wold et son père Herman Wold ; on utilise fréquemment l'abréviation anglaise régression PLS (« Partial Least Squares regression » et/ou « Projection to Latent Structure »).La régression PLS maximise la variance des prédicteurs (X i) = X et maximise la corrélation entre X et la variable à expliquer Y. A least squares problem may be solved directly with the optim function ; in this case the function NDcost may be useful to compute the derivatives (see the NDcost help page which provides a simple example for parameters identification of a differential equation). Examples . We will show different calling possibilities of leastsq on one (trivial) example which is non linear but does not really.

Linear vs

LeastSquares - PHP-ML - Machine Learning library for PH

  1. Thu-Hien To, Matthieu Jung, Samantha Lycett, Olivier Gascuel. Fast Dating Using Least-Squares Criteria and Algorithms. Systematic Biology, Oxford University Press (OUP), 2016, 65 (1), pp.82-97. 10.1093/sysbio/syv068
  2. imize the python numpy scipy statsmodels least-squares. asked Apr 16 at 0:31. Victor José. 367 3 3 silver badges 11 11 bronze badges. 0. votes. 2answers 46 views Piecewise linear fit. I am trying to.
  3. least_squares. Newer interface to solve nonlinear least-squares problems with bounds on the variables. See method=='lm' in particular. Notes leastsq is a wrapper around MINPACK's lmdif and lmder algorithms. cov_x is a Jacobian approximation to the Hessian of the least squares objective function. This approximation assumes that the objective function is based on the difference between.

Least-Squares Regression - YouTub

  1. Noté /5. Retrouvez Numerical Methods for Least Squares Problems et des millions de livres en stock sur Amazon.fr. Achetez neuf ou d'occasio
  2. g least squares fits to experimental data using linear combinations of functions. The data may be weighted or unweighted, i.e. with known or unknown errors. For weighted data the functions compute the best fit parameters and their associated covariance matrix. For.
  3. Least-Squares Policy Iteration: optimisme et compromis biais-variance pour le contrôle optimal. Journées Francophones de Planification, Décision et Apprentissage pour la conduite de systèmes, Jun 2010, Besançon, France. ￿inria-00520843￿ Least-SquaresλPolicy Iteration : optimisme et compromis biais-variance pour le controle optimalˆ Christophe Thiery, Bruno Scherrer LORIA - INRIA.

Leastsquares — Indicateurs et Signaux — TradingVie

Least Squares - MATLAB & Simulink - MathWork

Linear least squares may mean:. Linear least squares (mathematics), concerning the mathematics and computational aspects of the corresponding optimisation problem Linear regression, concerning the statistical context in which linear least squares sometimes arises.Special cases are: Simple linear regression; Ordinary least squares; Weighted least squares Least squares problems fall into two categories: linear or ordinary least squares and non-linear 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 Featured Least Squares free downloads and reviews at WinSit

The least squares (LSQR) algorithm is an adaptation of the conjugate gradients (CG) method for rectangular matrices. Analytically, LSQR for A*x = b produces the same residuals as CG for the normal equations A'*A*x = A'*b , but LSQR possesses more favorable numeric properties and is thus generally more reliable [1] Least Squares Optimization The following is a brief review of least squares optimization and constrained optimization techniques,which are widely usedto analyze and visualize data. Least squares (LS)optimiza-tion problems are those in which the objective (error) function is a quadratic function of the parameter(s) being optimized. The solutions to such problems may be computed analytically.

A texture atlas is an efficient way to represent information (like colors, normals, displacement maps) on triangulated surfaces. The LSCM method (Least Squares Conformal Maps) automatically generates a texture atlas from a meshed model. For large charts (over 100k facets), the convergence of the numerical solver may be slow. It is well known that the conformality criterion, minimized by. Many translated example sentences containing least-squares best fit - French-English dictionary and search engine for French translations Linear Least Squares Computations, Farebrother, Routledge. Des milliers de livres avec la livraison chez vous en 1 jour ou en magasin avec -5% de réduction

scipy.optimize.least_squares — SciPy v1.5.1 Reference Guid

  1. Estimation of the sky signal from sequences of time ordered data is one of the key steps in cosmic microwave background (CMB) data analysis, commonly referred to as the map-making problem. Some of the most popular and general methods proposed for this problem involve solving generalised least-squares (GLS) equations with non-diagonal noise weights given by a block-diagonal matrix with Toeplitz.
  2. Research Internship in which I was tasked to explore Machine Learning solutions for the detection of planetary transits in lightcurves, to offer an alternative to Box least Squares algorithms. I successfully conceived and implemented a prototype neural network, LightNet, able of both detection and classification, while significantly reducing the computational time compared to previous methods
  3. A macroeconometric model with 24 equations is built and estimated by two-stage least squares. Its short and long run properties are analysed by compunting multipliers and elasticities. It is then simulated. Its resolution is done by using space state representation and Kalman filter process. The practicability of these techniques is illustrated for the Moroccan case and sheds light on the.
  4. Textbook solution for Mathematical Excursions (MindTap Course List) 4th Edition Richard N. Aufmann Chapter 13.5 Problem 4ES. We have step-by-step solutions for your textbooks written by Bartleby experts
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  6. MATH 225N Week 8 Assignment Linear Regression Equations. Livres populaires. Affaires.Com. Civilisation en dialogues - Odile Grand-Clémen
  7. L'approximation parcimonieuse consiste à ajuster un modèle de données linéaire au sens des moindres carrés avec un faible nombre de composantes non nulles (la ``norme'' L0). En raison de sa complexité combinatoire, ce problème d'optimisation est souvent abordé par des méthodes sous-optimales. Il a cependant récemment été montré que sa résolution exacte était envisageable au.

WLS, weighted least squares regression using the confidence interval as weight. Shading indicates 95% confidence levels around the WLS. The slope of the WLS regression is shown; significant trends were identified using Mann-Kendall analyses. Kattegat. For the period 2006-2014, annual stratified-season means in dissolved oxygen concentration were lower than for the other assessment areas of. regression - traduction français-anglais. Forums pour discuter de regression, voir ses formes composées, des exemples et poser vos questions. Gratuit

Découvrez et achetez Nonparametric Statistics. Livraison en Europe à 1 centime seulement When a matrix A is square with full rank there is a vector that satisfies the equation for any . However when A is not square or does not have full rank such an may not exist because b does not lie in the range of A. In this case called the least squares problem we seek the vector x that minimizes the length (or norm) of the residual vector

JPT Incorporating Constraints Improves Least-Squares

Least Squares Regression Line of Best Fit. Imagine you have some points, and want to have a line that best fits them like this:. We can place the line by eye: try to have the line as close as possible to all points, and a similar number of points above and below the line Least squares fitting Linear least squares. Most fitting algorithms implemented in ALGLIB are build on top of the linear least squares solver: Polynomial curve fitting (including linear fitting) Rational curve fitting using Floater-Hormann basis Spline curve fitting using penalized regression splines And, finally, linear least squares fitting itself First three methods are important special. Abstract: This discussion is directed to least-squares estimation theory, from its inception by Gauss1 to its modern form, as developed by Kalman.2 To aid in furnishing the desired perspective, the contributions and insights provided by Gauss are described and related to developments that have appeared more recently (that is, in the 20th century). In the author's opinion, it is enlightening to. In the moving least squares (MLS) method one obtains a best approximation of the given data in a (moving) weighted least-squares sense. The computational burden is now shifted, and one needs to solve many small linear systems. Recently we have employed the theory of approx-imate approximations (see [12]) to develop a completely matrix-free approximate MLS approximation algorithm. So far we. Least-squares applications • least-squares data fitting • growing sets of regressors • system identification • growing sets of measurements and recursive least-squares 6-1. Least-squares data fitting we are given: • functions f1,...,fn: S → R, called regressors or basis functions • data or measurements (si,gi), i = 1,...,m, where si ∈ S and (usually) m ≫ n problem: find.

a least-squares - Traduction en français - exemples

Two-Stage Least-Squares Regression data considerations. Data. The dependent and independent variables should be quantitative. Categorical variables, such as religion, major, or region of residence, need to be recoded to binary (dummy) variables or other types of contrast variables. Endogenous explanatory variables should be quantitative (not categorical). Assumptions. For each value of the. Least squares approximation redirects here. It is not to be confused with Least-squares function approximation. Part of a series on S.. Least squares problems usually incorporate some assumptions about the errors in the model. For example, we might have y i = x1e x2ti + i, where the errors {i} are assumed to arise from a single probability distribution, often the normal distribution. Associated with our model are the true parameters x1 and x2, but each time we collect data and solve the least-squares problem we only.

This page describes how to solve linear least squares systems using Eigen. An overdetermined system of equations, say Ax = b, has no solutions.In this case, it makes sense to search for the vector x which is closest to being a solution, in the sense that the difference Ax - b is as small as possible. This x is called the least square solution (if the Euclidean norm is used) (ISBN 978--471-59431-4, LCCN chap. 9.4 (« Recursive Least Squares »), p. 541 (en) Simon Haykin (en), Adaptive filter theory, Upper Saddle River, N.J, Prentice Hall, 2002, 920 p. (ISBN 978--13-048434-5, OCL Least-squares variance component estimation: theory and GPS applications Delft institute of Earth Observation and Space systems (DEOS), Delft University of Technology Keywords: Least-squares variance component estimation (LS-VCE), normal distribution, elliptically contoured distribution, MINQUE, BIQUE, REML Citation: Amiri-Simkooei, A.R. (2007. Rank-Deficient Least-Squares Problems. When we used the QR decomposition of a matrix to solve a least-squares problem, we operated under the assumption that was full-rank. This assumption can fall flat. In that case we revert to rank-revealing decompositions. Suitable choices are either the (1) SVD or its cheaper approximation, (2) QR with. least squares. Les articles liés : Controlling the spurious oscillations in a least squares formulation of the transport equation approximated with space-time finite element. Suivez-nous sur : Nos sites : istegroup.com : ouvrages français iste-sciences.com : Encyclopédie SCIENCES iste.co.uk : ouvrages anglais openscience.fr : revues en libre accès . ISTE OpenScience 27-37 St George's.

least-squares weighted - Traduction en français - exemples

Given {pi} and {p'i}, we present an algorithm for finding the least-squares solution of R and T, which is based on the singular value decomposition (SVD) of a 3 3 matrix. This new algorithm is compared to two earlier algorithms with respect to computer time requirements. Index Terms. Least-Squares Fitting of Two 3-D Point Sets . Computer systems organization. Architectures. Parallel. CurveFitting LeastSquares compute a least-squares approximation Calling Sequence Parameters Description Examples Calling Sequence LeastSquares( xydata , v , opts ) LeastSquares( xdata , ydata , v , opts ) Parameters xydata - list, Array, DataFrame, or.. Polynomials Least-Squares Fitting: Polynomials are one of the most commonly used types of curves in regression. The applications of the method of least squares curve fitting using polynomials are briefly discussed as follows. To obtain further information on a particular curve fitting, please click on the link at the end of each item. Or try. least-squares estimation: choose as estimate xˆ that minimizes kAxˆ−yk i.e., deviation between • what we actually observed (y), and • what we would observe if x = ˆx, and there were no noise (v = 0) least-squares estimate is just xˆ = (ATA)−1ATy Least-squares 5-12. BLUE property linear measurement with noise: y = Ax+v with A full rank, skinny consider a linear estimator of form. English: Illustration of linear least squares. Source: Travail personnel: Auteur: Sega sai: Conditions d'utilisation. En tant que détenteur du droit d'auteur, je publie cette œuvre sous les licences suivantes : Ce fichier est disponible selon les termes de la licence Creative Commons Attribution - Partage dans les Mêmes Conditions 3.0 (non transposée). Vous êtes libre : de partager.

Least-Squares Adaptive Control Using Chebyshev Orthogonal Polynomials Nhan Nguyen ∗ NASA Ames Research Center, Moffett Field, CA 94035 John Burken† NASA Dryden Flight Research Center, Edwards, CA 93523 Abraham Ishihara‡ Carnegie Mellon University Silicon Valley, Moffett Field, CA 94035 This paper presents a new adaptive control approach using Chebyshev orthogonal polynomials as basis. Alternating Least Squares (ALS) 15 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 •Instead of explicit rat Generalized Least Squares. Des milliers de livres avec la livraison chez vous en 1 jour ou en magasin avec -5% de réduction least-squares (4) Sort By: New Votes. Python/Scipy-implémentation de sigma de optimize.curve_fit dans optimize.leastsq ; Python: raccord gaussien à deux courbes avec moindres carrés non linéaires ; Ajustement de régression orthogonale dans la méthode des moindres carrés scipy. API de traduction; À propos de MyMemory; Se connecter.

Bayesian vs. least-squares inverse kinematics: simulation ..

‪Least-Squares Regression‬ 1.1.20 - PhET: Free online. Algorithme 'Least Squares' Selon les données entrées, il se peut qu'aucun résultat ne se dégage. Objectif : prévoir le résultat d'un élève essayant une 5 ème fois un même test de vocabulaire. Résultats obtenus à un même test. Liste des tentatives. Liste des résultats. Numéro de la tentative pour laquelle un résultat doit être trouvé : Références. Lortet, A. (2019. Linear least-squares in geodesy. Murigande, C. (Auteur). 1986. Thèse de l'étudiant: Doc types › Docteur en Sciences. Contenu connexe. Résultat de recherche. Two new methods for solving large scale least squares in geodetic surveying computations. Résultats de recherche: Contribution à un journal/une revue › Article. Recent algorithms for solving linear least squares problems in space. 3.1 Least Squares Conformal Maps The LSCM parameterization algorithm[16] generates a discrete approximant of a conformal map. A conformal map preserves the local isotropy (see Figure 2), which can be useful for many applications, such as texture mapping[16, 5] or remeshing[1]. LSCM minimizes the conformal energy E C of the map-ping U, defined by : E C(S) = Z S ∂U(s) ∂x +i ∂U(s) ∂y 2. Système d'information scientifique MIA classé par unité (UR, UMR) INRA | CONTACT | CREDIT | POLITIQUES DE CONFIDENTIALITE | OBJECTIFS | MODE D'EMPLOI | PROPOSER un nouvel élément | CORRIGER un élément | CONTACT | CREDIT | POLITIQUES DE CONFIDENTIALITE | OBJECTIFS | MODE D'EMPLOI | PROPOSER un nouvel élémen

Two Stage Least Squares - example - YouTube

least squares - traduction française - dictionnaire

noun plural Date: 1825 a method of fitting a curve to a set of points representing statistical data in such a way that the sum of the squares of the distances of the points from the curve is a minimu Least Squares. Links: Instructions: Supporting Material: Applet Doesn't Work? Feedback: Supporting Material: Use the right mouse button to add new points. Use the left mouse button to move points around. To delete a point, use the middle button or move it off the graph. Instructions: Please enter a matrix whose values are not too big (less than 5) or too small (greater than .1) Otherwise you. Least-Squares Means: The R Package lsmeans: Abstract: Least-squares means are predictions from a linear model, or averages thereof. They are useful in the analysis of experimental data for summarizing the effects of factors, and for testing linear contrasts among predictions. The lsmeans package (Lenth 2016) provides a simple way of obtaining least-squares means and contrasts thereof. It.

Using SPSS and PASW/Ordinary Least Squares Regression

Least Squares, Lasso, Ridge and ElasticNe

In particular, it leads to the least squares method of fitting curves to collections of data. This unit also introduces matrix eigenvalues and eigenvectors. Many calculations become simpler when working with a basis of eigenvectors. The determinant of a matrix is a number characterizing that matrix. This value is useful for determining whether a matrix is singular, computing its inverse, and. Find many great new & used options and get the best deals for Understanding Least Squares Estimation and Geomatics Data Analysis by John Olusegun Ogundare (2018, Hardcover) at the best online prices at eBay! Free shipping for many products Hidden page that shows all messages in a threa The least squares (LS) mean attack rates per week were 0.589, 0.675, and 0.593 for subjects receiving avoralstat 500 mg, avoralstat 300 mg, and placebo, respectively. Overall, 1 subject in each of the avoralstat groups and no subjects in the placebo group were attack-free during the 84-day treatment period. The LS mean duration of all confirmed attacks was 25.4, 29.4, and 31.4 hours for the. Méthodes des moindres carrés - LEAST SQUARES FIT Jan 2007 - Jun 2007. Développement, traduction de l'anglais et adaptation pour la recherche nucléaire Développement mathématique évolué sur la technique des moindres carrés Simulation numérique réelle en laboratoire et vérification des algorithmes. Polynômes de Legendre de la classe Sturm-Liouville équations différentielles.

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Least-squares approximation - definition of Least-squares

STAT-F-408 Statistique informatique/Computational Statistics : Chapitre 0. INTRODUCTION 0.1 STAT-F-408 Statistique informatique/Computational Statistics (M. Guy. Exemple 19 : Régression Multiple. Cet exemple est basé sur le fichier d'exemple Poverty.sta qui contient les données d'évolution de différents agrégats entre le recensement de 1960 et celui de 1970 sur une sélection aléatoire de 30 comtés américains. Ouvrez ce fichier de données à l'aide de la commande Ouvrir des Exemples du menu Fichier ; ce fichier se situe dans le répertoire. This paper describes weighted least squares state estimation method and investigates how the efficiency of WLS state estimation changes according to 4 parameters: number of measurements, measurement type, measurement weight and level of noise. Different simulation cases are tested on 3-bus system and IEEE 14-bus system. The results show that accurate estimates of system state can be obtained.

Compressive 3D ultrasound imaging using a single sensor
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