The speed of the Levenberg–Marquardt ~LM! nonlinear iterative least-squares method depends upon the choice of damping strategy when the fitted parameters are highly correlated. Additive damping with small damping increments and large damping decrements permits LM to efficiently solve difficult problems, including those that otherwise cause stagnation. © 1997 American Institute of Physics. @S089...

Face detection is an intelligent approach used in a variety of applications that identifies human faces digital images. This work presents new method which composes neural network and Techebycheve transforms for face detection. For feature extraction, Tchebychev transform was applied, discrete given different sampling patterns several samples here were performed on color A Levenberg-Marquardt b...

Taking a new choice of the LM parameter ? k = ? J T F ? with id="M2"> ? open="(" close="]" 0,2 , we give modified Levenb...

A well-organized scheduling method is needed to meet the time-varying power necessities. The distribution of in forthcoming days must be scheduled. system's accuracy extensively impinges on economic function and reliability. At peak load time, detaching procedure necessary for decreasing demand load. This complexity conquered by present system forecasting centered constraints which affect Predi...

We compare algorithms for fundamental matrix computation, which we classify into “a posteriori correction”, “internal access”, and “external access”. Doing experimental comparison, we show that the 7-parameter Levenberg-Marquardt (LM) search and the extended FNS (EFNS) exhibit the best performance and that additional bundle adjustment does not increase the accuracy to any noticeable degree.

In this article a modified Levenberg-Marquardt method coupled with a Kaczmarz strategy for obtaining stable solutions of nonlinear systems of ill-posed operator equations is investigated. We show that the proposed method is a convergent regularization method. Numerical tests are presented for a non-linear inverse doping problem based on a bipolar model.

We examine the local convergence of the Levenberg–Marquardt method for the solution of nonlinear least squares problems that are rank-deficient and have nonzero residual. We show that replacing the Jacobian by a truncated singular value decomposition can be numerically unstable. We recommend instead the use of subset selection. We corroborate our recommendations by perturbation analyses and num...