An “electronic nose” has been used for the detection of adulterations of sesame oil. The system, comprising 10 metal oxide semiconductor ensors, was used to generate a pattern of the volatile compounds present in the samples. Prior to different supervised pattern recognition treatments, eature extraction techniques were employed to choose a set of optimal discriminant variables. Principal compo...

BACKGROUND Hepatitis is a serious public health problem with increasing cases and property damage in Heng County. It is necessary to develop a model to predict the hepatitis epidemic that could be useful for preventing this disease. METHODS The autoregressive integrated moving average (ARIMA) model and the generalized regression neural network (GRNN) model were used to fit the incidence data ...

This paper presents a low cost reduced instruction set computer (RISC) implementation of an intelligent ultra fast charger for a nickel–cadmium (Ni–Cd) battery. The charger employs a genetic algorithm (GA) trained generalized regression neural network (GRNN) as a key to ultra fast charging while avoiding battery damage. The tradeoff between mean square error (MSE) and the computational burden o...

All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes ...

Trip distribution is the second important stage in the 4-step travel demand forecasting. The purpose of the trip distribution forecasting is to estimates the trip linkages or interactions between traffic zones for trip makers. The problem of trip distribution is of non-linear nature and Neural Networks (NN) are well suited for addressing the non-linear problems. This fact supports the use of ar...

We propose a novel homogeneous neural network ensemble approach called Generalized Regression Neural Network (GEFTS–GRNN) Ensemble for Forecasting Time Series, which is a concatenation of existing machine learning algorithms. GEFTS uses a dynamic nonlinear weighting system wherein the outputs from several base-level GRNNs are combined using a combiner GRNN to produce the final algorithm appears...

The use of an artificial neural network (ANN) in many practical complicated problems encourages its implementation in the digital human modeling (DHM) world. DHM problems are complicated and need powerful tools like ANN to provide acceptable solutions. Human posture prediction is a DHM field that has been studied thoroughly in recent years. This work focuses on using a general regression neural...

The research was aimed at predicting floor water-inrush risk in coal mines and forewarn of such accidents to guide safe production practice. To this end, a prediction method for water inrush combining the chaotic fruit fly optimization algorithm (CFOA) generalized regression neural network (GRNN) is proposed. Floor predicted by virtue robust nonlinear mapping capability GRNN. However, because e...

Monitoring grain protein content in large areas by remote sensing is very important for guiding graded harvest, and facilitates grain purchasing for processing enterprises. Wheat grain protein content (GPC) at maturity was measured and multitemporal Landsat TM and Landsat ETM + images at key stages in 2003, 2004 growth stages were acquired in this study. GPC was estimated with multi-temporal re...