The paper presents a semismooth inexact Newton-type method for solving optimal power flow (OPF) problem. By introducing the nonlinear complementarity problem (NCP) function, the Karush-KuhnTucker (KKT) conditions of OPF model are transformed equivalently into a set of semismooth nonlinear algebraic equations. Then the set of semismooth equations can be solved by an improved inexact LevenbergMar...

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...

In this paper a novel technique is proposed to design Bowtie using Artificial Neural Networks (ANN). ANN models are developed to calculate the antenna Gain for the given frequency and dimensions. ANN is designed using Feed forward back propagation neural network (FFBPNN) architecture and trained by LevenbergMarquardt training algorithm. ANN can be trained to provide the best and worst case prec...

We review the integration between the genetic and evolutionary techniques with artificial neural networks. A Lamarckian model is proposed based on genetic algorithms and artificial neural networks. The genetic algorithm evolves the population while the artificial neural network performs the learning process. The direct encoding scheme was used. This model was submitted to several data sets and ...

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...

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 ...

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...

In this paper, two different neural models are proposed for calculating the quasi-static parameters of multilayer cylindrical coplanar waveguides and strip lines. These models were basically developed by training the artificial neural networks with the numerical results of quasi-static analysis. Neural models were trained with four different learning algorithms to obtain better performance and ...

Foreign exchange rate prediction is a stimulating research area from past decade. There are several statistical and machine learning methods already have been proposed by the researchers for foreign exchange rate prediction which provide better results. These models performed a vital role in future financial decision making which is taken by financial department administration of that country a...

Adaptation of network weights using LevenbergMarquardt (LM) training algorithm was proposed as a mechanism to improve the performance of Artificial Neural Networks (ANN) in modeling the Tennessee Eastman (TE) chemical process reactor. A Neural Network of the AutoRegressive, eXternal (NNARX) input model was developed. Four sub-models for the TE reactor were built. They are: the reactor level, th...