This work approaches the problem of estimating Kautz optimal poles in kernel expansion Wiener-Volterra models. The analytical solution for suboptimal case is already established literature. However, two parameters that compose still open. In this paper, an optimization strategy using Levenberg-Marquardt presented. algorithm used to find parameters, with same base all dimensions. construction ba...

In this paper we develop a convergence analysis in an infinite dimensional setting of the Levenberg–Marquardt iteration for the solution of a hybrid conductivity imaging problem. The problem consists in determining the spatially varying conductivity from a series of measurements of power densities for various voltage inductions. Although this problem has been very well studied in the literature...

A new algorithm for the solution of large-scale nonlinear complementarity problems is introduced. The algorithm is based on a nonsmooth equation reformulation of the complementarity problem and on an inexact Levenberg-Marquardt-type algorithm for its solution. Under mild assumptions, and requiring only the approximate solution of a linear system at each iteration, the algorithm is shown to be b...

Based on the perspective view of non-linear model fitting, a new algorithm for space resection based on Levenberg-Marquardt algorithm was developed in this paper. The relationship between the new algorithm and the current one, which is commonly implemented in the commercial software, was also discussed. The experimental evaluation of both algorithms with different level of inaccurate initial ap...

The Quasi-Linear Geostatistical Approach is a method of inverse modeling to identify parameter fields, such as the hydraulic conductivity in heterogeneous aquifers, given observations of related quantities like hydraulic heads or arrival times of tracers. Derived in the Bayesian framework, it allows to rigorously quantify the uncertainty of the identified parameter field. Since inverse modeling...

The problem is considered of the estimation of a polygonal region in two dimensions from data approximately marking the outline of the region. A solution is sought by formulating and solving a nonlinear least squares problem. A Levenberg–Marquardt method is developed for this problem, with an implementation which exploits the special structure so that the Levenberg–Marquardt step can be compute...

Arti3cial neural networks (ANNs) have recently gained attention as fast and 5exible vehicles to microwave modeling, simulation, and optimization. In this study, ANNs, based on the multilayer perceptron, were presented for accurate computation of the quasistatic parameters of asymmetric coplanar waveguides (ACPWs). Multilayer perceptron neural networks (MLPNNs) were trained with backpropagation,...

The objective of this study is to develop artificial neural network (ANN) models, including multilayer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM), for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP) representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to...

The task to compute 3D reconstructions from large amounts of data has become an active field of research within the last years. Based on an initial estimate provided by structure from motion, bundle adjustment seeks to find a solution that is optimal for all cameras and 3D points. The corresponding nonlinear optimization problem is usually solved by the Levenberg-Marquardt algorithm combined wi...