The present work is aimed at modelling and characterizing viscoelastic materials by means of a time domain technique. A constitutive equation containing internal variables is proposed. The parameter estimation problem is solved in time domain by means of the Levenberg-Marquadt technique. The effectiveness of the time domain estimation is assessed experimentally for three different structres.

A new method for the solution of the generalized complementarily problem is introduced. The method is based on a no smooth equation reformulation of the generalized complementarily problem and on a no smooth Levenberg-Marquardt method for its solution. The method is shown to be globally convergent. Numerical results are also given.

In this paper, a numerical method is proposed for canonical polyadic (CP) decomposition of small size tensors. The focus is primarily on decomposition of tensors that correspond to small matrix multiplications. Here, rank of the tensors is equal to the smallest number of scalar multiplications that are necessary to accomplish the matrix multiplication. The proposed method is based on a constrai...

The Levenberg-Marquardt (LM) minimization algorithm commonly employed in MOSFET model parameter extraction has several known deficiencies, such as poor convergence characteristics without a good initial guess, low likelihood of convergence to the globally optimal solution, and difficulty with simultaneous multiobjective optimizations. Furthermore, conventional tools require an expert user with ...

Nowadays the process of surgical planning is a crucial point of every operation in the craniofacial region. In this work we focus on the planning of graft reconstructive surgery for autologous osseous grafts. The planning method consists of two stages. The non-automatic graft design step is followed by a fully automatic procedure to find the best harvesting site in the predefined donor region. ...

This paper presents an innovative method to evaluate the credibility of network connections based on neural network with back propagation (BP) network model and Levenberg-Marquardt (LM) optimization algorithm. In it, the second-level index values are taken as input and expected evaluation values as output, based on which, L test samples are used to test the trained neural network. At last, a pr...

A recurrent neural network-based nonlinear model predictive control (NMPC) scheme in parallel with PI control loops is developed for a simulation model of an industrial-scale five-stage evaporator. Input-output data from system identification experiments are used in training the network using the Levenberg-Marquardt algorithm with automatic differentiation. The same optimization algorithm is us...

Abstract—Levenberg-Marquardt method (LM) was proposed to be applied as a non-linear least-square fitting in the analysis of a natural gamma-ray spectrum that was taken by the Hp (Ge) detector. The Gaussian function that composed of three components, main Gaussian, a step background function and tailing function in the lowenergy side, has been suggested to describe each of the -ray lines mathem...

Resumen: En este trabajo se presentan los resultados experimentales del control neuronal de velocidad de un servosistema. La estrategia de control neuronal implementada es el control por modelo inverso. El entrenamiento de la red se ha realizado usando dos algoritmos de aprendizaje: Levenberg-Marquardt y regularización Bayesiana. Se evalúa la capacidad de generalización de cada método en funció...

Obtaining 3D models from long image sequences is a major issue in computer vision. One of the main tools used to obtain accurate structure and motion estimates is bundle adjustment. Bundle adjustment is usually performed using nonlinear Newton-type optimizers such as Levenberg-Marquardt which might be quite slow when handling a large number of points or views. We investigate an algorithm for bu...