Radial basis functions are tools for reconstruction of mul-tivariate functions from scattered data. This includes, for instance, reconstruction of surfaces from large sets of measurements, and solving partial diierential equations by collocation. The resulting very large linear N N systems require eecient techniques for their solution, preferably of O(N) or O(N log N) computational complexity. ...

Abstract. Radial basis function (RBF) approximation has the potential to provide spectrally accurate function approximations for data given at scattered node locations. For smooth solutions, the best accuracy for a given number of node points is typically achieved when the basis functions are scaled to be nearly flat. This also results in nearly linearly dependent basis functions and severe ill...

This paper presents an axiomatic approach for constructing radial basis function (RBF) neural networks. This approach results in a broad variety of admissible RBF models, including those employing Gaussian RBF's. The form of the RBF's is determined by a generator function. New RBF models can be developed according to the proposed approach by selecting generator functions other than exponential ...

Nonnegative Matrix Factorization (NMF) is a feature extraction technique that has already found numerous applications in machine learning and data processing. In some applications, the feature vectors or lateral components can be modeled as linear combinations of basis functions. In this paper, we are concerned with modeling the features with Gaussian Radial Basis Functions (GRBF) that have bec...

A radial basis function (RBF) has the general form

In this paper, we have implemented a radial basis function (RBF) based method for solving the Black–Scholes partial differential equation. The application we have chosen is the valuation of European call options based on several underlying assets. We have shown that by appropriate choices of the RBF shape parameter and the node point placement, the accuracy of the results can be improved by at ...

1 Radial Basis Functions 2 1.1 Multivariate Interpolation and Positive Definiteness . . . . . . 3 1.2 Stability and Scaling . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Solving Partial Differential Equations . . . . . . . . . . . . . . 7 1.4 Comparison of Strong and Weak Problems . . . . . . . . . . . 8 1.5 Collocation Techniques . . . . . . . . . . . . . . . . . . . . . . 10 1.6 Method ...

In this paper, we consider multivariate interpolation with radial basis functions of finite smoothness. In particular, we show that interpolants by radial basis functions in R with finite smoothness of even order converge to a polyharmonic spline interpolant as the scale parameter of the radial basis functions goes to zero, i.e., the radial basis functions become increasingly flat.

We demonstrate a relationship between the singular values of the design matrix and the discrete Fourier transform of the radial function for radial basis function networks. We then show how regularisation leads to high frequency filtering of the network output. In certain circumstances, this allows the network parameters to be chosen a priori to appropriately bias the learning process.

For radial basis function interpolation of scattered data in IR d , the approximative reproduction of high-degree polynomials is studied. Results include uniform error bounds and convergence orders on compact sets. x1. Introduction We consider interpolation of real-valued functions f deened on a set IR d ; d 1. These functions are interpolated on a set X := fx 1 ; : : : ; x N X g of N X 1 pairw...