Sonomyography (SMG) is the signal we previously termed to describe muscle contraction using real-time muscle thickness changes extracted from ultrasound images. In this paper, we used least squares support vector machine (LS-SVM) and artificial neural networks (ANN) to predict dynamic wrist angles from SMG signals. Synchronized wrist angle and SMG signals from the extensor carpi radialis muscle...

One of the main obstructions in Multi-Access systems is the Multi-Access Interference (MAI) between signals that sharing the same channel. Specially, in the CDMA systems where all users share the same channel all the time. The objective of this work is to compare three different Artificial Neural Network (ANN)-based multiuser detectors for Wideband Code Division Multiple Access WCDMA system bui...

This study focuses on development, characterization and validation of an artificial neural network (ANN) model for prediction of advanced oxidation of organics in water matrix. The different ANNs, based on multilayer perceptron (MLP) and radial basis function (RBF) methodologies, have been applied for modeling of the behavior of complex system; zero-valent iron activated persulfate oxidation (F...

background: municipal solid waste (msw) is the natural result of human activities. msw generation modeling is of prime im­portance in designing and programming municipal solid waste management system. this study tests the short-term pre­diction of waste generation by artificial neural network (ann) and principal component-regression analysis. methods: two forecasting techniques are presented in...

In this paper we engineer an information mapping of transmission linkages across various European government bond markets. The research introduces a calibration methodology for the application of an optimizing radial basis function (RBF) artificial neural network (ANN). Utilizing a closed-form derivation of the regularization parameter, the Kajiji-4 RBF ANN is known to efficiently minimize the ...

The monitoring and determination of peanut maturity are fundamental to reducing losses during digging operation. However, the methods currently used laborious subjective. To solve this problem, we developed models access using images from unmanned aerial vehicles (UAV) satellites. We evaluated an area approximately 8 hectares in which a regular grid 30 points was determined with weekly evaluati...

We explore the potential of an artificial neural network (ANN) based method intelligence to probe propagation cosmic $\gamma$-ray photons in extragalactic Universe. The journey $\gamma$-rays emitted from a distant source like blazar observer at Earth is impeded by absorption through interaction with background light (EBL), leading electron-positron pair production. This process dominates for ga...

The purpose of this study is to model the nonparametric realized volatility of the futures contract as traded in domestic U.S. markets for exchange involving the South African rand and the U.S. dollar (ZAR). The study embraces a Bayesian regularization radial basis function (RBF) artificial neural network (ANN) to model the complex volatility patterns. The modeling characteristics revealed by t...

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...