At least implicitly, functions are the daily concern of most engineers and scientists. When they are not very smooth, i.e. when they do not have a significant number of derivatives, they can be ...

Abstract: In this paper we consider the approximation of functions by radial basic function interpolants. There is a plethora of results about the asymptotic ...

Abstract: One of the most important properties of neural nets (NNs) for control purposes is the universal approximation property. Unfortunately,, this property is generally proven for continuous ...

Polynomial approximation constitutes a fundamental framework in numerical analysis and applied mathematics, where complex functions are represented by simpler polynomial forms. A central pillar of ...

Mathematics of Computation, Vol. 84, No. 294 (JULY 2015), pp. 1835-1860 (26 pages) The Padé approximation has a long and rich history of theory and application and is known to produce excellent local ...

Neural network approximation techniques have emerged as a formidable approach in computational mathematics and machine learning, providing robust tools for approximating complex functions. By ...

ApproximationToolbox features low-rank tensors (including canonical, tensor-train and tree-based tensor formats), sparse tensors, polynomials, wavelets, and allows the plug-in of other approximation ...

This is a preview. Log in through your library . Abstract Gaussian process models have been widely used in spatial statistics but face tremendous computational challenges for very large data sets. The ...

Ask the publishers to restore access to 500,000+ books. A line drawing of the Internet Archive headquarters building façade. An illustration of a heart shape "Donate to the archive" An illustration of ...