Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/102878
DC FieldValue
dc.titleApproximation of minimum energy curves
dc.contributor.authorQu, R.
dc.contributor.authorYe, J.
dc.date.accessioned2014-10-28T02:30:45Z
dc.date.available2014-10-28T02:30:45Z
dc.date.issued2000-02-15
dc.identifier.citationQu, R.,Ye, J. (2000-02-15). Approximation of minimum energy curves. Applied Mathematics and Computation 108 (2-3) : 153-166. ScholarBank@NUS Repository.
dc.identifier.issn00963003
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/102878
dc.description.abstractThe problem of interpolating or approximating a given set of data points obtained empirically by measurement frequently arises in a vast number of scientific and engineering applications, for example, in the design of airplane bodies, cross sections of ship hull and turbine blades, in signal processing or even in less classical things like flow lines and moving boundaries from chemical processes. All these areas require fast, efficient, stable and flexible algorithms for smooth interpolation and approximation to such data. Given a set of empirical data points in a plane, there are quite a few methods to estimate the curve by using only these data points. In this paper, we consider using polynomial least squares approximation, polynomial interpolation, cubic spline interpolation, exponential spline interpolation and interpolatory subdivision algorithms. Through the investigation of a lot of examples, we find a 'reasonable good' fitting curve to the data. © 2000 Elsevier Science Inc. All rights reserved.
dc.sourceScopus
dc.subjectApproximation
dc.subjectMinimal energy curve
dc.subjectSpline
dc.subjectSubdivision algorithms
dc.typeArticle
dc.contributor.departmentMATHEMATICS
dc.description.sourcetitleApplied Mathematics and Computation
dc.description.volume108
dc.description.issue2-3
dc.description.page153-166
dc.description.codenAMHCB
dc.identifier.isiutNOT_IN_WOS
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