the khorasan region is one of the active seismic zones in iranian plateau, which is located between central iran and kopet-dagh zones. we study the crustal thickness (h) and vp/vs ratio (k) in mashhad, capital of razavi khorasan province by the receiver function method. this study uses teleseismic events recorded by an array of three broadband stations of khorasan seismic network, earthquake re...

In this paper we consider the problem of approximating functions from noisy data. We propose an incremental supervised learning algorithm for RBF networks. Hidden gaussian nodes are added in an iterative manner during the training process. For each new node added, the activation function center and the output connection weight are settled according to an extended chained version of the Nadaraja...

This paper proposes the use of radial basis function (RBF) networks in the modelling of non-linear engine processes. A pertinent application of such a model is the reconstruction of cylinder pressure based upon the instantaneous angular velocity of the engine crankshaft. Distinction is made between parametric and non-parametric models and applications to which each is suited. The structure of a...

In this paper, we propose a methodology for training a new model of artificial neural network called the generalized radial basis function (GRBF) neural network. This model is based on generalized Gaussian distribution, which parametrizes the Gaussian distribution by adding a new parameter t. The generalized radial basis function allows different radial basis functions to be represented by upda...

We formulate a phenomenological model for the segregation of binary mixtures of rough and smooth granular materials in a rotating drum. Our model successfully replicates a range of experimental behaviours, e.g., rapid radial segregation; slow axial segregation; and nonuniform radial structure of axial bands. We present some analytical results and detailed numerical results for our model.

Change-prone classes or modules are defined as software components in the source code which are likely to change in the future. Change-proneness prediction is useful to the maintenance team as they can optimize and focus their testing resources on the modules which have a higher likelihood of change. Change-proneness prediction model can be built by using source code metrics as predictors or fe...

In this paper, we investigated an ensemble neural network for the prediction of oil prices. Daily data from 1999 to 2012 were used to predict the West Taxes, Intermediate. Data were separated into four phases of training and testing using different percentages and obtained seven sub-datasets after implementing different attribute selection algorithms. We used three types of neural networks: Fee...

Rotorcraft system identification involves the derivation of the model structure and parameters, such as aerodynamic stability and control derivatives, fromflight-test results. Accurate rotorcraft system identification often requires higher ordermathematical models. However, system identification becomes difficult and complicated as the number of modeling degrees of freedom are increased. In the...

a 56-year-old man with a two year history of chronic stable angina underwent elective coronary artery bypass grafting (cabg) due to angiographic report of three vessel disease and tight stenosis at proximal part of left anterior descending artery (lad). while harvesting of radial artery (ra), the distal half of radial artery was found to bifurcate to two parallel branches with equal size. we us...

An approach to apply ensembles of regression models, built over the chunks of a data stream, to aid in residential premises valuation was proposed. The approach consists in incremental expanding an ensemble by systematically generated models in the course of time. The output of aged component models produced for current data is updated according to a trend function reflecting the changes of pre...