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

package smile.math.rbf;

import smile.math.Function;

/**
 * A radial basis function (RBF) 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 */ public interface RadialBasisFunction extends Function { }





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