Publisher review:Scattered Data Interpolation and Approximation using Radial Base Functions - Set of functions that can be used for interpolation and and approximation of scattered data Radial base functions (RBF) can be used for interpolation and and approximation of scattered data i.e. data is not required to be on any regular grid. The same function can handle data interpolation in any dimension. See file rbftest.m for more examples. 1. Create RBF interpolation using rbf=rbfcreate(x, f); ?x? ? coordinates of the nodes and ?f? - values of the function at the nodes2. Calculate interpolated values ?fi? at nodes ?xi? using fi = rbfinterp(xi, rbf); rbf ? is structure returned by rbf=rbfcreate(x, f) examplex = 0:1.25:10; f = sin(x); xi = 0:.1:10; %Matlab interpolationfi = interp1(x,f,xi); % RBF interpolationrbf=rbfcreate(x, f);fi = rbfinterp(xi, rbf);- examplex = rand(50,1)*4-2; y = rand(50,1)*4-2; z = x.*exp(-x.^2-y.^2);ti = -2:.05:2; [XI,YI] = meshgrid(ti,ti);%Matlab interpolationZI = griddata(x,y,z,XI,YI,'cubic');%RBF interpolationrbf=rbfcreate([x'; y'], z');ZI = rbfinterp([XI(:)'; YI(:)'], op);ZI = reshape(ZI, size(XI)); Optional parameters:1. Radial Base Function: rbfcreate(x, f ,'RBFFunction', 'multiquadric'); available RBF functions are: multiquadric, gaussian, linear, cubic, thinplate2. Smoothing level: (must be a positive scalar)rbfcreate(x, f ,'RBFSmooth', 0.1); 3. Multiquadric and gaussian functions have definable constants rbfcreate(x, f ,?RBFConstant', 0.1);RBF interpolation usually produces much better results that standard Matlab functions but computation complexity of RBF interpolation is n^3 thus it is not recommended to use it for more then 2000 nodes.
Scattered Data Interpolation and Approximation is a Matlab script for Mathematics scripts design by Alex Chirokov.
It runs on following operating system: Windows / Linux / Mac OS / BSD / Solaris.
Operating system:Windows / Linux / Mac OS / BSD / Solaris
Scattered Data Interpolation and Approximation script details 
