In this paper the neural network based lter for nonlinear interference cancellation is developed. The Hyper Radial Basis Function (HRBF) network with associated Manhattan learning algorithm is proposed for non-linear noise cancellation under assumption that reference noise is available. The HRBF network is a generalization of radial basis function (RBF) and generalized radial basis function (GR...

Functional equivalence of radial basis function (RBF) networks and a class of fuzzy inference systems is considered. The class of fuuy systems based on the Takagi-Sugeno model is referred to as TS-model of fuzzy inference. From the abstract mathematical viewpoint the functional equivalence between radial basis function networks and fuzzy inference systems is already shown. However, from the vie...

Radial basis function (RBF) methods have shown the potential to be a universal grid free method for the numerical solution of partial differential equations. Both global and compactly supported basis functions may be used in the methods to achieve a higher order of accuracy. In this paper, we take advantage of the grid free property of the methods and use an adaptive algorithm to choose the loc...

This paper aims to survey our recent work relating to the radial basis function (RBF) from some new views of points. In the first part, we established the RBF on numerical integration analysis based on an intrinsic relationship between the Green's boundary integral representation and RBF. It is found that the kernel function of integral equation is important to create efficient RBF. The fundame...

The Gaussian radial basis function (RBF) is a widely used kernel in kernel-based methods. parameter RBF, referred to as the shape parameter, plays an essential role model fitting. In this paper, we propose method select parameters for general RBF kernel. It can simultaneously serve variable selection and regression estimation. For former, asymptotic consistency established; latter, estimation e...

In this paper we will discuss a 1D learner based on RBF (Radial Basis Functions) networks. It differs from the classical RBF networks by the selection of cluster centers dependent of class classification in the training set. After the method is described, it is compared to other methods (KNN, RBF) and improvement suggestions are made.

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, du...

| Successful implementations of radial-basis function (RBF) networks for classiication tasks must deal with architectural issues, the burden of irrelevant attributes, scaling , and some other problems. This paper addresses these issues by initializing RBF networks with decision trees that deene relatively pure regions in the instance space; each of these regions then determines one basis functi...