Description The Gaussian kernel is a popular choice for radial basis function approximation. For a fixed dimension, the error decays exponentially with sample size. However, the leading coefficient of...
Description In this talk we introduce a generalization of the classical L2(Rd)-based Sobolev spaces with the help of a vector operator P which consists of finitely or countably many operators Pn that...
Description We consider the problem of multivariate interpolation with radial basis functions consisting of a scaling parameter. In particular, we discuss the limit of interpolants by radial basis...
Description In computer experiments where each run is costly, one may wish to construct a surrogate, f (x) = Ψ T(x)β, that approximates the true response f : Ω → R. Here Ψ(x) =(Ψ1 (x), . . . , Ψk (x))...
Description First train a neural network in MATLAB with data (253 samples with 16 parameters each) provided by the company and give a brief experimental result analysis. Then based on this result...
