Radial basis function methods are modern ways to approximate multivariate functions,
especially in the absence of grid data. They have been known, tested and analysed for …

We construct a new class of positive definite and compactly supported radial functions which
consist of a univariate polynomial within their support. For given smoothness and space …

Many practical applications require the reconstruction of a multivariate function from
discrete, unstructured data. This book gives a self-contained, complete introduction into this …

Meshfree approximation methods are a relatively new area of research, and there are only a
few books covering it at present. Whereas other works focus almost entirely on theoretical …

For interpolation of scattered multivariate data by radial basis functions, an “uncertainty
relation” between the attainable error and the condition of the interpolation matrices is …

This textbook is designed for graduate students in mathematics, physics, engineering, and
computer science. Its purpose is to guide the reader in exploring contemporary …

Deterministic Learning Theory for Identification, Recognition, and Control presents a unified
conceptual framework for knowledge acquisition, representation, and knowledge utilization …

Kernels are valuable tools in various fields of numerical analysis, including approximation,
interpolation, meshless methods for solving partial differential equations, neural networks …

In this paper, identification algorithms whose convergence and rate of convergence hinge
on the regressor vector being persistently exciting are discussed. It is then shown that if the …

A hierarchical scheme is presented for smoothly interpolating scattered data with radial
basis functions of compact support. A nested sequence of subsets of the data is computed …