Linear and nonlinear approximation of spherical radial basis function networks

Approximation of nonlinear systems with radial basis function neural networks

A technique for approximating a continuous function of n variables with a radial basis function (RBF) neural network is presented. The method uses an n-dimensional raised-cosine type of RBF that is smooth, yet has compact support. The RBF network coefficients are low-order polynomial functions of the input. A simple computational procedure is presented which significantly reduces the network tr...

Universal Approximation Using Radial-Basis-Function Networks

Function Approximation Using Robust Radial Basis Function Networks

Resistant training in radial basis function (RBF) networks is the topic of this paper. In this paper, one modification of Gauss-Newton training algorithm based on the theory of robust regression for dealing with outliers in the framework of function approximation, system identification and control is proposed. This modification combines the numerical robustness of a particular class of non-quad...

Radial Basis Function Networks and Nonlinear Data Modelling

Seven different Radial Basis Functions have been applied in a Feedforward Neural Network and tested on five different real or simulated multivariate modelling problems. A short theory of Radial Basis Functions is presented as well as the particular implementation of the Radial Basis Function Network (RBFN). The real world data modelling problems are; identifying the dynamic actuator characteris...

On the use of back propagation and radial basis function neural networks in surface roughness prediction

Various artificial neural networks types are examined and compared for the prediction of surface roughness in manufacturing technology. The aim of the study is to evaluate different kinds of neural networks and observe their performance and applicability on the same problem. More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, ...