We examine the ability of radial basis functions (RBFs) to generalize. We compare the performance of several types of RBFs. We use the inverse dynamics of an idealized two-joint arm as a test case. We find that without a proper choice of a norm for the inputs, RBFs have poor generalization properties. A simple global scaling of the input variables greatly improves performance. We suggest some e...

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. ...

We show how conditionally negative deenite functions on spheres coupled with strictly completely monotone functions (or functions whose derivative is strictly completely monotone) can be used for Hermite interpolation. The classes of functions thus obtained have the advantage over the strictly positive deenite functions studied in 17] that closed form representations (as opposed to series expan...

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...

Solving partial diierential equations by collocation with radial basis functions can be eeciently done by a technique rst proposed by E. Kansa in 1990. It rewrites the problem as a generalized interpolation problem, and the solution is obtained by solving a (possibly large) linear system. The method has been used successfully in a variety of applications, but a proof of nonsingularity of the li...