Name
Abstract | ||
|---|---|---|
Based on radial basis functions approximation, we develop in this paper a new com-putational algorithm for numerical differentiation.
Under an a priori and an a posteriori choice rules for the regularization parameter, we also give a proof on the convergence error estimate in reconstructing the
unknown partial derivatives from scattered noisy data in multi-dimension. Numerical examples verify that the proposed regularization
strategy with the a posteriori choice rule is effective and stable to solve the numerical differential problem.
|
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/s10444-005-9001-0 | Adv. Comput. Math. |
Keywords | Field | DocType |
numerical differentiation,radial basis functions,Tikhonov regularization,65D25,45D05,35R25 | Tikhonov regularization,Numerical differentiation,Radial basis function network,Mathematical optimization,Radial basis function,Mathematical analysis,Basis function,Mathematics | Journal |
Volume | Issue | ISSN |
27 | 3 | 1019-7168 |
Citations | PageRank | References |
3 | 0.82 | 8 |
Authors | ||
2 | ||