Abstract— Electromyography (EMG) signal provides a significant source of information for identification of neuromuscular disorders. This paper presents an application of neural network classifier on classification and identification of different normal and auto aggressive actions of hands and legs. Eight features that are extracted from eight channel EMG signals representing these actions have ...

In this paper, we present a barrier method for solving nonlinear programming problems. It employs a Levenberg-Marquardt perturbation to the Karush-Kuhn-Tucker (KKT) matrix to handle indefinite Hessians and a line search to obtain sufficient descent at each iteration. We show that the Levenberg-Marquardt perturbation is equivalent to replacing the Newton step by a cubic regularization step with ...

The Levenberg-Marquardt algorithm is one of the most popular algorithms for the solution of nonlinear least squares problems. In this paper, we propose and analyze the global and local convergence results of a novel Levenberg-Marquadt method for solving general nonlinear least squares problems. The proposed algorithm enjoys strong convergence properties (global convergence as well as quadratic ...

For solving nonsmooth systems of equations, the Levenberg-Marquardt method and its variants are of particular importance because of their locally fast convergent rates. Finitely manymaximum functions systems are very useful in the study of nonlinear complementarity problems, variational inequality problems, Karush-Kuhn-Tucker systems of nonlinear programming problems, and many problems in mecha...

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...

We propose a new self-adaptive Levenberg-Marquardt algorithm for the system of nonlinear equations F(x) = 0. The Levenberg-Marquardt parameter is chosen as the product of ‖Fk‖ with δ being a positive constant, and some function of the ratio between the actual reduction and predicted reduction of the merit function. Under the local error bound condition which is weaker than the nonsingularity, w...

Retrieving information from remotely sensed data is an important task. In the present work, data of L band microwave radiometer has been used to collect the brightness temperature over bare and vegetated fields in two polarizations at different moisture levels. Artificial neural network (ANN) trained with Levenberg-Marquardt algorithm has been used to determine soil moisture from brightness tem...

According to the structure of the BP neural network and the algorithm, choose three methods of BP neural network algorithm was improved, through analysis and comparison, computing speed is faster, more accurate judgment Levenberg Marquardt algorithm as the improved algorithm of optimal; Using the algorithm to the established BP neural network for training analysis; Then use the Matlab software,...