We describe and analyse Levenberg–Marquardt methods for solving systems of nonlinear equations. More specifically, we first propose an adaptive formula for the Levenberg–Marquardt parameter and analyse the local convergence of the method under Hölder metric subregularity. We then introduce a bounded version of the Levenberg–Marquardt parameter and analyse the local convergence of the modified m...

Gaussian decomposition of images leads to many promising applications in computer graphics. Gaussian representations can be used for image smoothing, motion analysis, and feature selection for image recognition. Furthermore, image construction from a Gaussian representation is fast, since the Gaussians only need to be added together. The most optimal algorithms [3, 6, 7] minimize the number of ...

In this paper, a new adjustment to the damping parameter of the Levenberg-Marquardt algorithm is proposed to save training time and to reduce error oscillations. The damping parameter of the Levenberg-Marquardt algorithm switches between a gradient descent method and the Gauss-Newton method. It also affects training speed and induces error oscillations when a decay rate is fixed. Therefore, our...

The objective of this study is to compare the predictive ability of Bayesian regularization with Levenberg–Marquardt Artificial Neural Networks. To examine the best architecture of neural networks, the model was tested with one-, two-, three-, four-, and five-neuron architectures, respectively. MATLAB (2011a) was used for analyzing the Bayesian regularization and Levenberg–Marquardt learning al...

Two kinds of the Levenberg-Marquardt-type methods for the solution of vertical complementarity problem are introduced. The methods are based on a nonsmooth equation reformulation of the vertical complementarity problem for its solution. Local and global convergence results and some remarks about the two kinds of the Levenberg-Marquardt-type methods are also given. Finally, numerical experiments...

Primary/Lead Author David Bogle (editor); Engell; Ross & Pistikopoulos; Binder, Abel & Marquardt; Petersen, Jorgensen & Skogestad; Mathison; Backx, Bosgra & Marquardt; Hovd & Skogestad. Identification TWG3/D Bogle/R&D SoA Review/Rev P/1998-01-12 Purpose Discussion paper providing an overview of the state of the art in Research into the various aspects of Flexibility, Operability & Dynamics iden...

In this paper a modification on Levenberg-Marquardt algorithm for MLP neural network learning is proposed. The proposed algorithm has good convergence. This method reduces the amount of oscillation in learning procedure. An example is given to show usefulness of this method. Finally a simulation verifies the results of proposed method. Keywords—Levenberg-Marquardt, modification, neural network,...