Regularized Minimum Length Method in Scattered Data Interpolation
نویسندگان
چکیده
منابع مشابه
Scattered data approximation of fully fuzzy data by quasi-interpolation
Fuzzy quasi-interpolations help to reduce the complexity of solving a linear system of equations compared with fuzzy interpolations. Almost all fuzzy quasi-interpolations are focused on the form of $widetilde{f}^{*}:mathbb{R}rightarrow F(mathbb{R})$ or $widetilde{f}^{*}:F(mathbb{R})rightarrow mathbb{R}$. In this paper, we intend to offer a novel fuzzy radial basis function by the concept of so...
متن کاملA Novel Triangle-based Method for Scattered Data Interpolation
Local numerical methods for scattered data interpolation often require a smart subdivision of the domain in geometrical polyhedral structures. In particular triangulations in the plane (2D) and tetrahedrizations in the space (3D) are widely used to define interpolation models. In this paper we give a short survey on the main methods for the scattered data problem and we recall preliminaries on ...
متن کاملEnergy minimization method for scattered data Hermite interpolation
Given a set of scattered data with derivatives values, we use a minimal energy method to find Hermite interpolation based on bivariate spline spaces over a triangulation of the scattered data locations. We show that the minimal energy method produces a unique Hermite spline interpolation of the given scattered data with derivative values. Also we show that the Hermite spline interpolation conve...
متن کاملScattered data fitting using least squares with interpolation method
Scattered data fitting is a big issue in numerical analysis. In many applications, some of the data are contaminated by noise and some are not. It is not appropriate to interpolate the noisy data, and the traditional least squares method may lose accuracy at the points which are not contaminated. In this paper, we present least squares with interpolation method to solve this problem. The existe...
متن کاملScattered Data Interpolation in N-Dimensional Space
Radial Basis Functions (RBF) interpolation theory is briefly introduced at the “application level” including some basic principles and computational issues. The RBF interpolation is convenient for un-ordered data sets in n-dimensional space, in general. This approach is convenient especially for a higher dimension N 2 conversion to ordered data set, e.g. using tessellation, is computationally v...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied and Computational Mathematics
سال: 2014
ISSN: 2328-5605
DOI: 10.11648/j.acm.20140304.17