Abstract Traditional atmospheric models are based on the analysis and fitting of various factors influencing space atmosphere density. Neural network do not specifically analyze polynomials each factor in model, but use large data sets for construction. Two traditional model algorithms analyzed, main affecting identified, an neural networks containing is proposed. According to simulation error,...

The inverse problem in acousto-electric tomography concerns the reconstruction of electric conductivity a body from knowledge power density function interior body. This results currents prescribed at boundary electrodes, and it can be obtained through electro-static measurements together with auxiliary acoustic probing. Previous works on used continuum model for electrostatic conditions; howeve...

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 ...

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.

Abstract: In testing digital waveform recorders, an important part is to fit a sinusoidal model to recorded data, and calculate the parameters that result in the best fit. Methods are already standardized; however, they demand high computational power. In this article a new, quick and accurate sinefitting algorithm will be shown based on Levenberg-Marquardt (LM) method. The constraints of conve...

In order to effectively implement a good model based control strategy, the combination of different linear models working at various operating regions are mostly utilised since a single model that can operate in that fashion is always a difficult task to develop. This work presents the use of soft computing approaches such as evolutional algorithm called simulated annealing (SA), a genetic algo...

In this paper we present di0erent inversion algorithms for nonlinear ill-posed problems arising in atmosphere remote sensing. The proposed methods are Landweber’s method (LwM), the iteratively regularized Gauss–Newton method, and the conventional and regularizing Levenberg–Marquardt method. In addition, some accelerated LwMs and a technique for smoothing the Levenberg–Marquardt solution are pro...

Abstract: Our purpose of this paper is to solve a class of stochastic linear complementarity problems (SLCP) with finitely many elements. Based on a new stochastic linear complementarity problem function, a new semi-smooth least squares reformulation of the stochastic linear complementarity problem is introduced. For solving the semi-smooth least squares reformulation, we propose a feasible non...