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


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
Your Session has timed out. Please sign back in to continue.





Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In