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/******************************************************************************
 *                   Confidential Proprietary                                 *
 *         (c) Copyright Haifeng Li 2011, All Rights Reserved                 *
 ******************************************************************************/

/**
 * Radial basis functions. A radial basis function is a real-valued function
 * whose value depends only on the distance from the origin, so that
 * φ(x)=φ(||x||); or alternatively on the distance from some other
 * point c, called a center, so that φ(x,c)=φ(||x-c||). Any function
 * φ that satisfies the property is a radial function. The norm is usually
 * Euclidean distance, although other distance functions are also possible.
 * For example by using probability metric it is for some radial functions
 * possible to avoid problems with ill conditioning of the matrix solved to
 * determine coefficients wi (see below), since the ||x|| is always
 * greater than zero.
 * 

* Sums of radial basis functions are typically used to approximate given * functions: *

* y(x) = Σ wi φ(||x-ci||) *

* where the approximating function y(x) is represented as a sum of N radial * basis functions, each associated with a different center ci, and weighted * by an appropriate coefficient wi. The weights wi can * be estimated using the matrix methods of linear least squares, because * the approximating function is linear in the weights. *

* This approximation process can also be interpreted as a simple kind of neural * network and has been particularly used in time series prediction and control * of nonlinear systems exhibiting sufficiently simple chaotic behavior, * 3D reconstruction in computer graphics (for example, hierarchical RBF). * * @author Haifeng Li */ package smile.math.rbf;





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