This paper describes artificial neural network (ANN) based prediction of theresponse of a fiber optic sensor using evanescent field absorption (EFA). The sensingprobe of the sensor is made up a bundle of five PCS fibers to maximize the interaction ofevanescent field with the absorbing medium. Different backpropagation algorithms areused to train the multilayer perceptron ANN. The Levenberg-Marq...

The channel estimation algorithms play a vital role in third generation (3G) communication systems to support efficient spectrum utilization. The transition from 3G to 4G systems is to provide high data rate, error free low complexity system with efficient adaptive techniques. A noisy Channel estimation and feedback error introduces imperfect Channel State Information (CSI). This paper introduc...

This paper presents generation of a compact thermal model of a Ball Grid Array (BGA) based on experimental test results obtained from infrared (IR) camera system. The model is optimized so that the steady state and transient thermal behaviors of the package may be predicted with required accuracy. The optimization algorithm is based on Gauss-Newton and Levenberg-Marquardt methods which are well...

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 aim of the study is to find right architecture NARX neural network, in order perform daily prediction maximum wind speed Laayoune city. We relied on Levenberg-Marquardt optimization algorithm. RMSE error metric showed that NARX-SP outperforms NARX-P.

We study the global behaviour of a Newton algorithm on the Grassmann manifold for invariant subspace computation. It is shown that the basins of attraction of the invariant subspaces may collapse in case of small eigenvalue gaps. A Levenberg-Marquardt-like modification of the algorithm with low numerical cost is proposed. A simple strategy for choosing the parameter is shown to dramatically enl...

We present a novel method for estimating the fundamental matrix, a key problem arising in stereo vision. The method aims to minimise a cost function that is derived from maximum likelihood considerations. The respective minimiser turns out to be significantly more accurate than the familiar algebraic least squares technique. Furthermore, the method is identical in accuracy to a Levenberg-Marqua...

We present a novel method for estimating the fundamental matrix, a key problem arising in stereo vision. The method aims to minimise a cost function that is derived from maximum likelihood considerations. The respective minimiser turns out to be significantly more accurate than the familiar algebraic least squares technique. Furthermore, the method is identical in accuracy to a Levenberg-Marqua...

A new deconvolution method for Auger electron spectroscopy is presented. This method is based on a non-linear least squares minimizing routine (Levenberg-Marquardt) and global approximation using splines, solving many of the drawbacks inherent to the Van Cittert and Fourier transform based deconvolution methods. The deconvolution routine can be run on a personal computer. The application of thi...

We describe a generalized Levenberg-Marquardt method for computing critical points of the Ginzburg-Landau energy functional which models superconductivity. The algorithm is a blend of a Newton iteration with a Sobolev gradient descent method, and is equivalent to a trust-region method in which the trustregion radius is defined by a Sobolev metric. Numerical test results demonstrate the method t...