in this paper, we propose a parametric uniform approximation method to solve np-hard absolute value equations. for this, we uniformly approximate absolute value in such a way that the nonsmooth absolute value equation can be formulated as a smooth nonlinear equation. by solving the parametric smooth nonlinear equation using newton method, for a decreasing sequence of parameters, we can get the ...

An approach is described to the numerical solution of order conditions for RungeKutta methods whose solutions evolve on a given manifold. This approach is based on least squares minimization using the Levenberg-Marquardt algorithm. Examples of methods of order four, ve and six are given and numerical experiments are presented which con rm that the derived methods have the expected order of accu...

Inverse modeling seeks model parameters given a set of observations. However, for practical problems because the number of measurements is often large and the model parameters are also numerous, conventional methods for inverse modeling can be computationally expensive. We have developed a new, computationally efficient parallel Levenberg-Marquardt method for solving inverse modeling problems w...

Abstract This paper presents a parallel approach to the Levenberg-Marquardt algorithm (LM). The use of train neural networks is associated with significant computational complexity, and thus computation time. As result, when network has big number weights, becomes practically ineffective. article new computations in learning algorithm. proposed solution based on vector instructions effectively ...

For proper planning and optimisation of radio network coverage quality at the mobile station terminals, prediction of propagation path loss with reasonable accuracy is important. This research work proposes the application of a hybrid neural modelling technique to predict of signal coverage propagation losses in typical urban environment. The modelling technique is based on combining a conventi...

In this paper, we propose a parametric uniform approximation method to solve NP-hard absolute value equations. For this, we uniformly approximate absolute value in such a way that the nonsmooth absolute value equation can be formulated as a smooth nonlinear equation. By solving the parametric smooth nonlinear equation using Newton method, for a decreasing sequence of parameters, we can get the ...

Nonnegative Matrix Factorization (NMF) solves the following problem: find nonnegative matrices A ∈ RM×R + and X ∈ RR×T + such that Y ∼= AX, given only Y ∈ RM×T and the assigned index R. This method has found a wide spectrum of applications in signal and image processing, such as blind source separation, spectra recovering, pattern recognition, segmentation or clustering. Such a factorization is...