MÉTODOS DE INTERPOLAÇÃO DE SHEPARD BASEADO EM NÚCLEOS / KERNEL BASED SHEPARD`S INTERPOLATION METHOD
AUTOR(ES)
JOANA BECKER PAULO
DATA DE PUBLICAÇÃO
2009
RESUMO
Several real problem in computational modeling require function approximations. In some cases, the function to be evaluated in the computer is very complex, so it would be nice if this function could be substituted by a simpler and efficient one. To do so, the function f is sampled in a set of N pontos {x1, x2, . . . , xN}, where x(i) (is an element of) R(n), and then an estimate for the value of f in any other point is done by an interpolation method. An interpolation method is any procedure that takes a set of constraints and determines a nice function that satisfies such conditions. The Shepard interpolation method originally calculates the estimate of F(x) for some x (is an element of) R(n) as a weighted mean of the N sampled values of f. The weight for each sample xi is a function of the negative powers of the euclidian distances between the point x and xi. Kernels K : R(n) ×R(n) (IN) R are functions that correspond to an inner product on some Hilbert space F that contains the image of the points x and z by a function phi (the empty set) : R(n) (IN) F, i.e. k(x, z) =
ASSUNTO(S)
metodo de interpolacao de shepard nucleos geometric modeling shepards interpolation method modelagem geometrica kernels