Least squares methods are effective for solving systems of partial differential equations. In the case of nonlinear systems the equations are usually linearized by a Newton iteration or successive substitution method, and then treated as a linear least squares problem. We show that it is often advantageous to form a sum of squared residuals first, and then compute a zero of the gradient with a ...

Performance of four types of functionally different artificial neural network (ANN) models, namely Feed forward neural network, Elman type recurrent neural network, Input delay neural network and Radial basis function network and fourteen types of algorithms, namely Batch gradient descent (traingd), Batch gradient descent with momentum (traingdm), Adaptive learning rate (traingda), Adaptive lea...

This paper presents a tensor approximation algorithm, based on the Levenberg–Marquardt method for nonlinear least square problem, to decompose large-scale tensors into sum of products vector groups given scale, or obtain low-rank without losing too much accuracy. An Armijo-like rule inexact line search is also introduced this algorithm. The result decomposition adjustable, which implies that ca...

We present regularization tools for training small-and-medium as well as large-scale artiicial feedforward neural networks. The determination of the weights leads to very ill-conditioned nonlinear least squares problems and regularization is often suggested to get control over the network complexity, small variance error, and nice optimization problems. The algorithms proposed solve explicitly ...

Abstract: The supply chain performance evaluation is a critical activity to continuously improve operations. Literature presents several systems based on multi-criteria methods and artificial intelligence. Among them, the neural networks (ANN) excel due their capacity of modeling non-linear relationships between metrics allowing adaptations specific environment by means historical data. These s...

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...

This paper considers the nonlinear systems modeling problem for control. A structured nonlinear parameter optimization method (SNPOM) adapted to radial basis function (RBF) networks and an RBF network-style coefficients autoregressive model with exogenous variable model parameter estimation is presented. This is an off-line nonlinear model parameter optimization method, depending partly on the ...

Abstract— Electromyography (EMG) signal provides a significant source of information for identification of neuromuscular disorders. This paper presents an application of neural network classifier on classification and identification of different normal and auto aggressive actions of hands and legs. Eight features that are extracted from eight channel EMG signals representing these actions have ...

For solving nonsmooth systems of equations, the Levenberg-Marquardt method and its variants are of particular importance because of their locally fast convergent rates. Finitely manymaximum functions systems are very useful in the study of nonlinear complementarity problems, variational inequality problems, Karush-Kuhn-Tucker systems of nonlinear programming problems, and many problems in mecha...