A new boundary hunting radial basis function (BH-RBF) classifier which allocates RBF centers constructively near class boundaries is described. This classifier creates complex decision boundaries only in regions where confusions occur and corresponding RBF outputs are similar. A predicted square error measure is used to determine how many centers to add and to determine when to stop adding cent...

this paper investigates the residual stress fields in the vicinity of weld bead in hsla-65 steel weldments using a neural network. this study consists of two cases: (i) the experimental analysis was carried out the measurements of residual stresses by xrd technique. many different specimens that were subjected to different conditions were studied. the values and distributions of residual stress...

A two-layer neural network is developed in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron model is formed by the composition of an adapted logistic function with the mean of the partial input-weight similarities. The model is capable of dealing directly with variables of potentially different nature (continuous, ordinal, categorical); t...

In this paper, we deal with the problem of time series prediction from a given set of input/output data. This problem consists of the prediction of future values based on past and/or present data. We present a new method for prediction of time series data using radial basis functions. This approach is based on a new efficient method of clustering of the centers of the radial basis function neur...

in the present study, an attempt has been made to use data envelopment analysis (dea) for assessing the technical efficiency and return-to-scale for greenhouse cucumber production in iran. for this purpose, the data from greenhouses in esfahan province, during one period of plant cultivation in one year including spring plants were randomly collected. the results indicated that total input ener...

In this paper we consider radial DEA models without inputs (or without outputs), and radial DEA models with a single constant input (or with a single constant output). We demonstrate that (i) a CCR model without inputs (or without outputs) is meaningless; (ii) a CCR model with a single constant input (or with a single constant output) coincides with the corresponding BCC model; (iii) a BCC mode...

The highly organized structure and composition of the annulus fibrosus provides the tissue with mechanical behaviors that include anisotropy and nonlinearity. Mathematical models are necessary to interpret and elucidate the meaning of directly measured mechanical properties and to understand the structure-function relationships of the tissue components, namely, the fibers and extrafibrillar mat...

Input selection in the nonlinear function approximation is important and difficult problem. Neural networks provide good generalization in many cases, but their interpretability is usually limited. However, the contributions of input variables in the prediction of output would be valuable information in many real world applications. In this work, an input selection algorithm for Radial basis fu...

This paper proposes an oriented Hausdorff similarity measure for object matching. The oriented Hausdorff distance (HD) measure is introduced by replacing the distance concept of conventional HD algorithms by a similarity measure. The orientation at each ~ i x e l is used for removing incorrect correspondences. Various experiments show that the perFigure 1: Block diagram for the Hausdorff distan...