1 – Introduction Parameter estimation for function optimization is a well established problem in computing, as there are countless applications in practice. For this work, we will focus specifically in implementing a distributed and parallel implementation of the Levenberg Marquardt algorithm, which is a well established numerical solver for function approximation given a limited data set. Para...

Prediction of the gas holdup and pressure drop in a horizontal pipe for gas-non-Newtonian liquid flow using Artificial Neural Networks (ANN) methodology have been reported in this paper from the data acquired from our earlier experiment. The ANN prediction is done using Multilayer Perceptrons (MLP) trained with three different algorithms, namely: Backpropagation (BP), Scaled Conjugate gradient ...

In our previous work we proposed some extensions of the Levenberg-Marquardt algorithm; the Bacterial Memetic Algorithm and the Bacterial Memetic Algorithm with Modified Operator Execution Order for fuzzy rule base extraction from inputoutput data. Furthermore, we have investigated fuzzy flip-flop based feedforward neural networks. In this paper we introduce the adaptation of the Bacterial Memet...

The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks. The algorithm is tested on several function approximation problems, and is compared with a conjugate gradient algorithm and a variable learning rate algorithm. It is found that the Marquardt algorithm is much more efficient than either...

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 ...

This report presents the studies carried out on two modifications suggested in the literature for Levenberg-Marquardt algorithm. The modifications are applicable to feed-forward neural networks. One modification [18], made on performance index, reduces computational complexity of the Levenberg-Marquardt algorithm, while the other one [17], made on calculation of the gradient information, improv...

Levenberg-Marquardt algorithm and conjugate gradient method are frequently used for optimization in multi-layer perceptron (MLP). However, both algorithms have mixed conclusions optimizing MLP time series forecasting. This study uses autoregressive integrated moving average (ARIMA) with method. These methods were to predict the Air Pollutant Index (API) Malaysia's central region where represent...

This work presents a theoretical framework in which health promotion and health education program implementation can be conceived as an open dynamic system. By tracing the evolution of an elementary school-based nutrition program from its conception to its recent form, we construct a program genealogy. Data were derived from two interviews and three historical documents from which historical ev...

Pre-execution uses helper threads running in spare hardware contexts to trigger cache misses in front of the main thread, hence hiding their latency. At the heart of pre-execution is the code that runs in the pre-execution threads themselves. The most common approach is for preexecution threads to run a subset of the instructions executed by the original program, called backward slices [18], wh...