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RESEARCH PAPERS

Smoothing Noisy Data Using Dynamic Programming and Generalized Cross-Validation

[+] Author and Article Information
C. R. Dohrmann, H. R. Busby

Department of Mechanical Engineering, The Ohio State University, Columbus, OH 43210

D. M. Trujillo

Trucomp, Fountain Valley, CA 92708

J Biomech Eng 110(1), 37-41 (Feb 01, 1988) (5 pages) doi:10.1115/1.3108403 History: Received February 09, 1987; Revised November 23, 1987; Online June 12, 2009

Abstract

Smoothing and differentiation of noisy data using spline functions requires the selection of an unknown smoothing parameter. The method of generalized cross-validation provides an excellent estimate of the smoothing parameter from the data itself even when the amount of noise associated with the data is unknown. In the present model only a single smoothing parameter must be obtained, but in a more general context the number may be larger. In an earlier work, smoothing of the data was accomplished by solving a minimization problem using the technique of dynamic programming. This paper shows how the computations required by generalized cross-validation can be performed as a simple extension of the dynamic programming formulas. The results of numerical experiments are also included.

Copyright © 1988 by ASME
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