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 ...

Unmanned Aircraft Systems (UAS) have been widely applied for reconnaissance and surveillance by exploiting the information collected from the digital imaging payload. However, the data analysis of UAS videos is frequently limited by motion blur; the frame-to-frame movement induced by aircraft roll, wind gusts, and less than ideal atmospheric conditions; and the noise inherent within the image s...

The aim of the study is to find right architecture NARX neural network, in order perform daily prediction maximum wind speed Laayoune city. We relied on Levenberg-Marquardt optimization algorithm. RMSE error metric showed that NARX-SP outperforms NARX-P.

We present an algorithm based on maximum likelihood for the estimation and renormalization (marginalization) of exponential densities. The moment-matching problem resulting from the maximization of the likelihood is solved as an optimization problem using the Levenberg-Marquardt algorithm. In the case of renormalization, the moments needed to set up the moment-matching problem are evaluated usi...

The Levenberg-Marquardt (LM) learning algorithm is a popular algorithm for training neural networks; however, for large neural networks, it becomes prohibitively expensive in terms of running time and memory requirements. The most time-critical step of the algorithm is the calculation of the Gauss-Newton matrix, which is formed by multiplying two large Jacobian matrices together. We propose a m...

In this paper we develop a convergence analysis in an infinite dimensional setting of the Levenberg–Marquardt iteration for the solution of a hybrid conductivity imaging problem. The problem consists in determining the spatially varying conductivity from a series of measurements of power densities for various voltage inductions. Although this problem has been very well studied in the literature...

This paper presents a novel training algorithm for fuzzy inference systems. The algorithm combines the Levenberg-Marquardt algorithm with variable structure systems approach. The combination is performed by expressing the parameter update rule in continuous time and application of sliding mode control method to the gradient based training procedure. The proposed combination therefore exhibits a...

The purpose of this study was to compare the performance of two methods for gravity inversion of a fault. First method [Particle swarm optimization (PSO)] is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. Second method [The Levenberg-Marquardt algorithm (LM)...