The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks. The algorithm is tested on several function approximation problems, and is compared with a conjugate gradient algorithm and a variable learning rate algorithm. It is found that the Marquardt algorithm is much more efficient than either...

This report presents the studies carried out on two modifications suggested in the literature for Levenberg-Marquardt algorithm. The modifications are applicable to feed-forward neural networks. One modification [18], made on performance index, reduces computational complexity of the Levenberg-Marquardt algorithm, while the other one [17], made on calculation of the gradient information, improv...

speak about the question of stability, in accordance with Marquardt and Medina. The court’s opinion in Marquardt, though relevant to a limited jurisdiction, presents several areas of potential impact on practice. On the one hand, it could serve to deter pursuit of more aggressive treatment for a patient by providers, as it imposes a potentially significant obstacle to obtaining a court order sh...

Financial forecasting is an example of signal processing problems. A number of ways to train/learn the network are available. We have used Levenberg-Marquardt algorithm for error back-propagation for weight adjustment. Pre-processing of data has reduced much of the variation at large scale to small scale, reducing the variation of training data. Keywords— Gradient descent method, jacobian matri...

Back propagation neural network (BPNN) algorithm is a widely used technique in training artificial neural networks. It is also a very popular optimization procedure applied to find optimal weights in a training process. However, traditional back propagation optimized with Levenberg marquardt training algorithm has some drawbacks such as getting stuck in local minima, and network stagnancy. This...

1 – Introduction Parameter estimation for function optimization is a well established problem in computing, as there are countless applications in practice. For this work, we will focus specifically in implementing a distributed and parallel implementation of the Levenberg Marquardt algorithm, which is a well established numerical solver for function approximation given a limited data set. Para...

Financial forecasting is an example of signal processing problems. A number of ways to train/learn the network are available. We have used Levenberg-Marquardt algorithm for error back-propagation for weight adjustment. Pre-processing of data has reduced much of the variation at large scale to small scale, reducing the variation of training data. Keywords— Gradient descent method, jacobian matri...

The problem is considered of the estimation of a polygonal region in two dimensions from data approximately marking the outline of the region. A solution is sought by formulating and solving a nonlinear least squares problem. A Levenberg–Marquardt method is developed for this problem, with an implementation which exploits the special structure so that the Levenberg–Marquardt step can be compute...

The rst part of this paper studies a Levenberg-Marquardt scheme for nonlinear inverse problems where the corresponding Lagrange (or regularization) parameter is chosen from an inexact Newton strategy. While the convergence analysis of standard implementations based on trust region strategies always requires the invertibility of the Fr echet derivative of the nonlinear operator at the exact solu...