We present a new constructive algorithm for building Radial-Basis-Function (RBF) network classiiers and a tree based associated algorithm for fast processing of the network. This method, named Constructive Tree Radial-Basis-Function (CTRBF), allows to build and train a RBF network in one pass over the training data set. The training can be in supervised or unsupervised mode. Furthermore, the al...

in many industrialized areas, the highest concentration of particulate matter, as a major concern on public health, is being felt worldwide problem. since the air pollution assessment and its evaluation with considering spatial dispersion analysis because of various factors are complex, in this paper, gis-based modeling approach was utilized to zoning pm2.5 dispersion over tehran, during one ye...

We propose a method for optimizing the complexity of Radial basis function (RBF) networks. The method involves two procedures: adaptation (training) and selection. The first procedure adaptively changes the locations and the width of the basis functions and trains the linear weights. The selection procedure performs the elimination of the redundant basis functions using an objective function ba...

We propose two localized Radial Basis Function (RBF) methods, the Radial Basis Function Partition of Unity method (RBF-PUM) and the Radial Basis Function generated Finite Differences method (RBF-FD), for solving financial derivative pricing problems arising from market models with multiple stochastic factors. We demonstrate the useful features of the proposed methods, such as high accuracy, spa...

Radial Basis Function (RBF) can be used for reconstruction of damaged images, filling gaps and for restoring missing data in images. Comparisons with standard method for image inpainting and experimental results are included and demonstrate the feasibility of the use of the RBF method for image processing applications.

Metamodeling for High Dimensional Simulation-based Design Problems Songqing Shan Dept. of Mech. and Manuf. Engineering University of Manitoba Winnipeg, MB, Canada R3T 5V6 [email protected] G. Gary Wang School of Engineering Science Simon Fraser University Surrey, BC, Canada V3T 0A3 [email protected] Abstract Computational tools such as finite element analysis and simulation are widely used i...

The Radial Basis Function method (RBF) can be used not only for reconstruction of a surface from scattered data but for reconstruction of damaged images, filling gaps and for restoring missing data in images, too. The basic idea of reconstruction algorithm with RBF and very interesting results from reconstruction of images damaged by noise are presented. Feasibility of the RBF method for image ...

| Adaptive learning dynamics of the Radial Basis Function Network (RBFN) are compared with a scale-based clustering technique Won93] and a relationship between the two is pointed out. Using this link, it is shown how scale-based clustering can be done using the RBFN, with the Radial Basis Function (RBF) width as the scale parameter. The technique suggests the \right" scale at which the given da...

Hierarchical RBF networks consist of multiple RBF networks assembled in different level or cascade architecture. In this paper, an evolved hierarchical RBF network was employed to detect the breast cancel. For evolving a hierarchical RBF network model, Extended Compact Genetic Programming (ECGP), a tree-structure based evolutionary algorithm and the Differential Evolution (DE) are used to find ...

We present a generalization of the RBF-FD method that allows full control of the overlap between RBF-FD stencils. We accomplish this by introducing a continuous overlap parameter δ ∈ [0, 1] such that δ = 1 recovers the standard RBF-FD method and δ = 0 results in a full decoupling of the RBF-FD stencils. We show with a simple example that global interpolation with both RBFs and RBFs augmented wi...