This research examined the applicability of using a neural network approach to analyze population pharmacokinetic data. Such data were collected retrospectively from pediatric patients who had received tobramycin for the treatment of bacterial infection. The information collected included patient-related demographic variables (age, weight, gender, and other underlying illness), the individual's...

in this study the wavelet neural network (wnn) and artificial neural network (ann) were used to simulate barley breakage percentage in combine harvester. the models have been trained using the same data conditions. air temperature, thresher cylinder speed, distance between thresher cylinder and concave (back and forth) and the percentage of barely moisture were as the input variables. the resul...

this paper presents an accurate differential global positioning system (dgps) using multi-layered neural networks (nns) based on the back propagation (bp) and imperialistic competition algorithm (ica) in order to predict the dgps corrections for accurate positioning. simulation results allowed us to optimize the nn performance in term of residual mean square error. we compare results obtained b...

In this work, we present an approach for fuzzy aggregation of neural networks forecasting. The interval type-3 aggregator is used to combine the outputs improve quality prediction. This carried out in such a way that final output better than individual modules. our approach, system estimate prediction increments will be assigned process combining them with set rules. uncertainty modeled system,...

The aim of this research is to develop a farmer prediction system to identify crop suitable for particular soil. To achieve this Neural Network should be trained to perform correct prediction for farmers. After the network has been properly trained, it can be used to identify the crop suitable for particular type of soil. The Artificial neural networks are relatively crude electronic networks o...

By combing the theories of the switched systems and the interval neural networks, the mathematics model of the switched interval neural networks with discrete and distributed timevarying delays of neural type is presented. A set of the interval parameter uncertainty neural networks with discrete and distributed time-varying delays of neural type are used as the individual subsystem, and an arbi...

Abstract | A new model for the field strength prediction for mobile communication networks inside buildings is presented in this paper. The model is based on the determination of the dominant paths between the transmitter and the receiver. The field strength is predicted with artificial neural networks, trained with measurements. In contrast to other neural prediction models a good generalizati...

Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death for Type 1 diabetes mellitus (T1DM) patients. Based on the T1DM patients’ physiological parameters, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval, we have developed a neural network based rule discovery system with...

Inferential estimation of polymer melt index in an industrial polymerisation process using aggregated neural networks is presented in this paper. The difficult-to-measure polymer melt index is estimated from the easy-to-measure process variables and their relationship is estimated using aggregated neural networks. The individual networks are trained on bootstrap re-samples of the original train...

The aim of this paper is to present deep neural network architectures and algorithms and explore their use in time series prediction. Existing and novel input variable selection algorithms and deep neural networks are applied for ultra-short-term wind prediction. Since gradient-based optimization starting from random initialization often appears to get stuck in poor solutions, recent research e...