A new deconvolution method for Auger electron spectroscopy is presented. This method is based on a non-linear least squares minimizing routine (Levenberg-Marquardt) and global approximation using splines, solving many of the drawbacks inherent to the Van Cittert and Fourier transform based deconvolution methods. The deconvolution routine can be run on a personal computer. The application of thi...

We propose a modification to the Levenberg-Marquardt minimization algorithm for a more robust and more efficient calibration of highly parameterized, strongly nonlinear models of multiphase flow through porous media. The new method combines the advantages of truncated singular value decomposition with those of the classical Levenberg-Marquardt algorithm, thus enabling a more robust solution of ...

Brain-Computer Interfaces (BCIs) measure brain signals activity, intentionally and unintentionally induced by users, and provides a communication channel without depending on the brain’s normal peripheral nerves and muscles output pathway. Feature Selection (FS) is a global optimization machine learning problem that reduces features, removes irrelevant and noisy data resulting in acceptable rec...

Increasing electricity demand in Java-Madura-Bali, Indonesia, must be addressed appropriately to avoid blackout by determining accurate peak load forecasting. Econometric approach may not be sufficient to handle this problem due to limitation in modelling nonlinear interaction of factors involved. To overcome this problem, Elman and Jordan Recurrent Neural Network based on Levenberg-Marquardt l...

Algorithms for the estimation of nonlinear regression parameters are considered. Adaptive population-based search algorithms are proposed and implemented in deriving reliable estimates at a reasonable time with default setting of their controlling parameters. The algorithms are tested on the NIST collection of datasets containing 27 nonlinear regression tasks of various level of difficulty. The...

For managing data in a smart card’s limited memory, containing medical and biometric images, images compression is resorted to. For image retrieval, it is necessary that the classification algorithm be efficient to search and locate the image in a compressed domain. This study proposes a novel training algorithm for Multi-Layer Perceptron Neural Network (MLP-NN) to classify compressed images. M...

Two trust-region methods for systems of mixed nonlinear equalities, general inequalities and simple bounds are proposed. The first method is based on a Gauss-Newton model, the second one is based on a regularized Gauss-Newton model and results to be a Levenberg-Marquardt method. The globalization strategy uses affine scaling matrices arising in bound-constrained optimization. Global convergence...

RNNs have local feedback loops within the network which allows them to shop earlier accessible patterns. This network can be educated with gradient descent back propagation and optimization technique such as second-order methods; conjugate gradient, quasi-Newton, Levenberg-Marquardt have also been used for networks training [14, 15]. But still this algorithm is not definite to find the global m...