In this paper, we propose a new updating rule of the LevenbergMarquardt (LM) parameter for the LM method for nonlinear equations. We show that the global complexity bound of the new LM algorithm is O( −2), that is, it requires at most O( −2) iterations to derive the norm of the gradient of the merit function below the desired accuracy . Host: Jiawang Nie Wednesday, November 1, 2017 4:00 PM AP&M...

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...

This work approaches the problem of estimating Kautz optimal poles in kernel expansion Wiener-Volterra models. The analytical solution for suboptimal case is already established literature. However, two parameters that compose still open. In this paper, an optimization strategy using Levenberg-Marquardt presented. algorithm used to find parameters, with same base all dimensions. construction ba...

The Levenberg-Marquardt (LM) gradient descent algorithm is used extensively for the training of Artificial Neural Networks (ANN) in the literature, despite its limitations, such as susceptibility to the local minima that undermine its robustness. In this paper, a bioinspired algorithm referring to the Bat algorithm was proposed for training the ANN, to deviate from the limitations of the LM. Th...

In this paper, we consider a nonlinear least squares model for the phase retrieval problem. Since the Hessian matrix may not be positive definite and the Gauss-Newton (GN) matrix is singular at any optimal solution, we propose a modified Levenberg-Marquardt (LM) method, where the Hessian is substituted by a summation of the GN matrix and a regularization term. Similar to the well-known Wirtinge...