Abstract—Levenberg-Marquardt method (LM) was proposed to be applied as a non-linear least-square fitting in the analysis of a natural gamma-ray spectrum that was taken by the Hp (Ge) detector. The Gaussian function that composed of three components, main Gaussian, a step background function and tailing function in the lowenergy side, has been suggested to describe each of the -ray lines mathem...

Materials and methods: A data set consisting of 584 stroke patients was analyzed using MLP neural networks. Th e eff ect of prognostic factors (age, hospitalization time, sex, hypertension, atrial fi brillation, embolism, stroke type, infection, diabetes mellitus, and ischemic heart disease) on mortality in stroke were trained with 6 diff erent MLP algorithms [quick propagation (QP), Levenberg-...

In this work we look for a general neural network model that resembles the interactions between glucose concentration levels and amount of insulin injected in the bodies of diabetics. We use real data for 70 different patients of diabetics and build on it our model. Two types of neural networks (NN’s) are experimented in building that model; the first type is called the Levenberg-Marquardt (LM)...

This article describes some of the capabilities encapsulated within the Model Independent Calibration and Uncertainty Analysis Toolbox (MICUT), which was written to support the popular PEST model independent interface. We have implemented a secant version of the Levenberg–Marquardt (LM) method that requires far fewer model calls for local search than the PEST LM methodology. Efficiency studies ...

Data mining technology is applied to extract the water supply operation rules in this study. Five characteristic attributes—reservoir storage water, operation period number, water demand, runoff, and hydrological year—are chosen as the dataset, and these characteristic attributes are applied to build a mapping relation with the optimal operation mode calculated by dynamic programming (DP). A Le...

We use natural gradient (NG) learning neural networks (NNs) for modeling and identifying nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter H followed by a zero-memory nonlinearity g(·). The NN model is composed of a linear adaptive filter Q followed by a two-layer memoryless nonlinear NN. A Kalman filter-based technique and a search-and-converge m...

A parameter-self-adjusting Levenberg-Marquardt method (PSA-LMM) is proposed for solving a nonlinear system of equations F (x) = 0, where F : R → R is a semismooth mapping. At each iteration, the LM parameter μk is automatically adjusted based on the ratio between actual reduction and predicted reduction. The global convergence of PSALMM for solving semismooth equations is demonstrated. Under th...

The viscosities of aqueous two phase system containing bovine serum albumin (BSA) were predicted by artificial neural network (ANN) as a function of concentration of poly-ethylene-glycol (PEG), concentration of BSA and temperature. A three layer feed forward neural network based on Levenberg-Marquardt (LM) algorithm which consisted of three input neurons, 10 hidden neurons and one output neuron...

In this paper, we propose a new approach to solving the Kruppa equations for camera self-calibration. Traditionally, the unknown scale factors in the Kruppa equations are eliminated first, leading to a set of nonlinear constraints. Instead, we determine the scale factors by a Levenberg-Marquardt (LM) optimization or Genetic optimization technique first. Then, the camera’s intrinsic parameters a...

Parametric chamfer alignment (PChA) is commonly employed for aligning an observed set of points with a corresponding set of reference points. PChA estimates optimal geometric transformation parameters that minimize an objective function formulated as the sum of the squared distances from each transformed observed point to its closest reference point. A distance transform enables efficient compu...