Sensitivity Functions and Their Uses in Inverse Problems
Abstract
In this note we present a critical review of the some of the positive features as well as some of the shortcomings of the generalized sensitivity functions "GSF" of Thomaseth-Cobelli in comparison to traditional sensitivity functions "TSF". We do this from a computational perspective of ordinary least squares estimation or inverse problems using two illustrative examples: the Verhulst-Pearl logistic growth model and a recently developed agricultural production network model. Because GSF provide information on the relevance of data measurements for the identification of certain parameters in a typical parameter estimation problems, we argue that they provide the basis for new tools for investigators in design of inverse problem studies.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 21, 2007
- Accession Number
- ADA471254
Entities
People
- H. Thomas Banks
- Sava Dediu
- Stacey L. Ernstberger
Organizations
- North Carolina State University