This study offers a description and comparison of the main models of Artificial Neural Networks (ANN) which have proved to be useful in time series forecasting, and also a standard procedure for the practical application of ANN in this type of task. The Multilayer Perceptron (MLP), Radial Base Function (RBF), Generalized Regression Neural Network (GRNN), and Recurrent Neural Network (RNN) model...

Over the last decade or so, artificial neural networks (ANNs) have become one of the most promising tools for modelling hydrological processes such as rainfall runoff processes. However, the employment of a single model does not seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process that varies in space and time. For this reason, this study aims at...

Models predicting aqueous solubility of benzylamine salts were developed using multivariate partial least squares (PLS) and artificial neural network (ANN). Molecular descriptors, including binding energy (BE) and surface area of salts (SA), were calculated by the use of Hyperchem and ChemPlus QSAR programs for Windows. Other physicochemical properties, such as hydrogen acceptor for oxygen atom...

Several neural network architectures have been developed over the past several years. One of the most popular and most powerful architectures is the multilayer perceptron. This architecture will be described in detail and recent advances in training of the multilayer perceptron will be presented. Multilayer perceptrons are trained using various techniques. For years the most used training metho...

MLP_DoA module, being an integral part of the smart TWAA DoA subsystem, intended for fast estimation is proposed. Multilayer perceptron network used to create module that provides a radio gateway location in azimuthal plane at its output when spatial correlation matrix, found by receiving signal using two-element textile wearable antenna array, on input. training with monitoring generalization ...

This paper presents a novel artificial neural network (ANN) model estimating vehicle-level radiated magnetic emissions of an electric car as a function of the corresponding driving pattern. Real world electromagnetic interference (EMI) experiments have been realized in a semi-anechoic chamber using Renault Twizy. Time-domain electromagnetic interference (TDEMI) measurement techniques have been ...

This paper presents a comparison between two Artificial Neural Network (ANN) approaches, namely, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) networks, in flood forecasting. The basic difference between the two methods is that the parameters of the former network are nonlinear and those of the latter are linear. The optimum model parameters are therefore guaranteed in the latter,...

This paper describes an experiment where classifiers are used to identify potential diagnoses on examining textual content of medical records. Three classifiers are applied separately (k-nearest neighborhood, multilayer perceptron and support vector machines) and also combined in two different approaches (parallel and cascading); results show that even accuracy point to a best alternative, ROC ...

The training algorithm studied in this paper is inspired by the biological metaplasticity property of neurons. Tested on different multidisciplinary applications, it achieves a more efficient training and improves Artificial Neural Network Performance. The algorithm has been recently proposed for Artificial Neural Networks in general, although for the purpose of discussing its biological plausi...

Removal of boilerplate is among the essential tasks in web corpus construction and web indexing. In this paper, we present an improved machine learning approach to general-purpose boilerplate detection for languages based on (extended) Latin alphabets (easily adaptable to other scripts). We keep it highly efficient (around 320 documents per single CPU core second) by using an optimized Multilay...