This paper reviews various optimization techniques available for training multi-layer perception (MLP) artificial neural networks for compression of images. These optimization techniques can be classified into two categories: Derivative-based and Derivative free optimization. The former is based on the calculation of gradient and includes Gradient Descent, Conjugate gradient, Quasi-Newton, Leve...

An optimization-based methodology is proposed in this paper preserving mesh surfaces in 3D watermarking. The LevenbergMarquardt optimization algorithm is used for displacing the vertices according to the message to be embedded. A specific cost function is used by this method in order to ensure minimal surface distortion while the watermark would be enabled with high robustness to attacks. This ...

The use of cubic splines, instead of polynomials, in representing static nonlinearities in block structured models is considered. A system identification algorithm for the Hammerstein structure, a static nonlinearity followed by a linear filter, is developed in which the static nonlinearity is represented by a cubic spline. The identification algorithm, based on a separable least squares Levenb...

The application of neural network (ANN) for the prediction of fermentation variables in batch fermenter for the production of ethanol from grape waste using Saccharomyces cerevisiae yeast has been discussed in this article. Artificial neural network model, based on feed forward architecture and back propagation as training algorithm, is applied in this study. The LevenbergMarquardt optimization...

Training neural networks is a complex task of great importance in the supervised learning field of research. We intend to show the superiority (time performance and quality of solution) of the new metaheuristic bat algorithm (BA) over other more ―standard‖ algorithms in neural network training. In this work we tackle this problem with five algorithms, and try to over a set of results that could...

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

We analyze the global convergence properties of some variants of regularized continuous Newton methods for convex optimization and monotone inclusions in Hilbert spaces. The regularization term is of LevenbergMarquardt type and acts in an open-loop or closed-loop form. In the open-loop case the regularization term may be of bounded variation.