ISSN 2277-5064 | © 2013 Bonfring Abstract--This paper presents a neural network based on Levenberg-Marquardt back-propagation algorithm for prediction of degree of angiographic coronary heart disease. The novelty of this work is training a one hidden layer neural network with Levenberg-Marquardt back-propagation algorithm for multivariate large dataset. An ANN model is developed for prediction ...

In this paper, we adopt the Levenberg-Marquardt (LM) algorithm to implement the nonlinear multivariable optimization for azimuth/elevation angle-of-arrival (AOA) estimation based on the Capon beamforming algorithm. The formulation is based on the fact that the cost function of the Capon algorithm can be expressed in a least-squares form. The performance in terms of the root mean square error (R...

This paper presents an efficient method for updating the structural finite element model. Model updating is performed through minimizing the difference of recorded acceleration of real damaged structure and hypothetical damaged structure, by updating physical parameters in each phase using iterative process of Levenberg-Marquardt algorithm. This algorithm is based on sensitivity analysis and pr...

The Quasi-Linear Geostatistical Approach is a method of inverse modeling to identify parameter fields, such as the hydraulic conductivity in heterogeneous aquifers, given observations of related quantities like hydraulic heads or arrival times of tracers. Derived in the Bayesian framework, it allows to rigorously quantify the uncertainty of the identified parameter field. Since inverse modeling...

In the present work, an artificial neural network (ANN) model was used to study the quantitative structure retention relationship (QSRR) of retention index (RI) of some volatile compounds in natural cocoa and conched chocolate powder. Molecular structural descriptors are selected using genetic algorithm to construct the nonlinear QSRR models, kernel partial least squares PLS (KPLS) and Levenber...

This paper proposes a new Levenberg-Marquardt algorithm that is accelerated by adjusting a Jacobian matrix and a quasi-Hessian matrix. The proposed method partitions the Jacobian matrix into block matrices and employs the inverse of a partitioned matrix to find the inverse of the quasi-Hessian matrix. Our method can avoid expensive operations and save memory in calculating the inverse of the qu...

In this paper, we propose the block-diagonal matrix to approximate the Hessian matrix in the Levenberg Mar-quardt method in the training of neural networks. Two weight updating strategies, namely asynchronous and synchronous updating methods were investigated. Asyn-chronous method updates weights of one block at a time while synchronous method updates all weights at the same time. Variations of...