Any feedforward artificial neural network (ANN) training procedure begins with the initialisation of the connection weights’ values. These initial values are generally selected in a random or quasi-random way in order to increase training speed. Nevertheless, it is common practice to initialize the same ANN architecture in a repetitive way in order for satisfactory training results to be achiev...

Artificial Neural Network (ANN) has found widespread application in the field of classification. Many domains have benefited with the use of ANN based models over traditional statistical models for their classification and prediction needs. Many techniques have been proposed to arrive at optimal values for parameters of the ANN model to improve its prediction accuracy. This paper compares the i...

In this work the electrooxidation half-wave potentials of some Benzoxazines were predicted from their structural molecular descriptors by using quantitative structure-property relationship (QSAR) approaches. The dataset consist the half-wave potential of 40 benzoxazine derivatives which were obtained by DC-polarography. Descriptors which were selected by stepwise multiple selection procedure ar...

this paper reports on the use of artificial neural networks (ann) and partial least squareregression (pls) combined with nir spectroscopy (900-1700 nm) to design calibration models for thedetermination of sugar content in sugar beet. in this study a total of 80 samples were used as the calibration set,whereas 40 samples were used for prediction. three pre-processing methods, including multiplic...

predictive quantitative structure–activity relationship was performed on the novel 4-oxo-1,4-dihydroquinoline and 4-oxo-4h-pyrido[1,2-a]pyrimidine derivatives to explore relationship between the structure of synthesized compounds and their anti-hiv-1 activities. in this way, the suitable set of the molecular descriptors was calculated and the important descriptors using the variable selections ...

Four car-following models with artificial neural network (ANN) structure were developed with various input variables in the car-following behavior. A four-layer ANN structure was set up and a genetic algorithm (GA) and back-propagation methodology were utilized for determining the synaptic weights in the models, however the models sometimes had a difficulty in learning such enormous number of r...

Most of the land use change modelers have used to model binary land use change rather than multiple land use changes. As a first objective of this study, we compared two well-known LUC models, called classification and regression tree (CART) and artificial neural network (ANN) from two groups of data mining tools, global parametric and local non-parametric models, to model multiple LUCs. The ca...

Over the last decade or so, artificial neural networks (ANNs) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. However, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. For this reason, this study aims at de...

One of the principal issues related to hydrologic models for prediction of runoff is the estimation of extreme values (floods). It is well understood that unless the models capture the dynamics of rainfall-runoff process, the improvement in prediction of such extremes is far from reality. In this paper, it is proposed to develop a dual (combined and paralleled) artificial neural network (D-ANN)...