This document is an introduction to radial basis function RBF networks a type of arti cial neural network for application to problems of supervised learning e g regression classi cation and time series prediction It is now only available in PostScript an older and now unsupported hyper text ver sion may be available for a while longer The document was rst published in along with a package of Ma...

Novel direct adaptive robust state and output feedback controllers are presented for the output tracking control of a class of nonlinear systems with unknown system dynamics and disturbances. Both controllers employ a variable-structure radial basis function (RBF) network that can determine its structure dynamically to approximate unknown system dynamics. Radial basis functions are added or rem...

Studies convergence properties of radial basis function (RBF) networks for a large class of basis functions, and reviews the methods and results related to this topic. The authors obtain the network parameters through empirical risk minimization. The authors show the optimal nets to be consistent in the problem of nonlinear function approximation and in nonparametric classification. For the cla...

Approximation methods are widely used in many fields and many techniques have been published already. This comparative study presents a comparison of LOWESS (Locally weighted scatterplot smoothing) and RBF (Radial Basis Functions) approximation methods on noisy data as they use different approaches. The RBF approach is generally convenient for high dimensional scattered data sets. The LOWESS me...

Radial basis function networks (RBF) are efficient general function approximators. They show good generalization performance and they are easy to train. Due to theoretical considerations RBFs commonly use Gaussian activation functions. It has been shown that these tight restrictions on the choice of possible activation functions can be relaxed in practical applications. As an alternative differ...

This paper describes an optimized training approach of radial basis function (RBF) classification by reducing a proposed classification-oriented error function. The training approach consists of two distinguished properties. First, radial basis functions, feature weights, and output weights can be updated iteratively; Second, it intrinsically distinguishes different learning contribution from t...

Radial basis function (RBF) approximations have been used for some time to in-terpolate data on a sphere (as well as on many other types of domains). Theirability to solve, to spectral accuracy, convection-type PDEs over a sphere has beendemonstrated only very recently. In such applications, there are two main choicesthat have to be made: (i) which type of radial function to...

Radial basis function (RBF) approximations have been used for some time to interpolate data on a sphere (as well as on many other types of domains). Their ability to solve, to spectral accuracy, convection-type PDEs over a sphere has been demonstrated only very recently. In such applications, there are two main choices that have to be made: (i) which type of radial function to use, and (ii) wha...

A solution of a nonlinear equalizer based on the radial basis functions neural network is given in this paper. We consider a bipolar signal which passes through a dispersive channel and is corrupted by additive noise. When the distortion caused by the channel is nonlinear, the classical methods fail. The task of signal recovery is viewed as a pattern recognition problem, where each transmitted ...

The purpose of this study is to identify the hierarchical radial basis function neural networks and select important input features for each sub-RBF neural network automatically. Based on the pre-defined instruction/operator sets, a hierarchical RBF neural network is created and evolved by using Extended Compact Genetic Programming (ECGP), and the parameters are optimized by Differential Evolut...