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...

In this note we study the convergence of the Levenberg-Marquardt regularization scheme for nonlinear ill-posed problems. We consider the case that the initial error satisfies a source condition. Our main result shows that if the regularization parameter does not grow too fast (not faster than a geometric sequence), then the scheme converges with optimal convergence rates. Our analysis is based ...

The potential of the product unit neural networks built by using different memetic evolutionary algorithms for the simultaneous determination of mixtures of analytes based on dynamic responses was investigated. For this purpose, three methodologies for obtaining the structure and weights of neural networks were proposed, based on the combination of the evolutionary programming algorithm, a clus...

We compare the two well-known global optimization methods, simulated annealing and genetic optimization, to a local gradient-based optimization technique. We rate the applicability of each method in terms of the minimal achievable target value for a given number of simulation runs in an inverse modeling application. The gradient-based optimizer used in the experiment is based on the Levenberg-M...

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...