๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Identification of non-linear parametrically varying models using separable least squares

โœ Scribed by Previdi *, F.; Lovera, M.

Book ID
120476734
Publisher
Taylor and Francis Group
Year
2004
Tongue
English
Weight
264 KB
Volume
77
Category
Article
ISSN
0020-7179

No coin nor oath required. For personal study only.

๐Ÿ“œ SIMILAR VOLUMES

Identification of linear time-varying sy
Identification of linear time-varying systems using a modified least-squares algorithm
โœ Rogelio Lozano; Dimitrios Dimogianopoulos; Robert Mahony ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 169 KB

In this paper a modi"ed version of the standard least-squares algorithm is presented. The aim is to use the proposed modi"ed LS algorithm in linear time-varying systems. The proposed modi"cation involves the addition of extra terms to both the parameter estimates' and the covariance's update laws. W

A note on fitting one-compartment models
A note on fitting one-compartment models: Non-linear least squares versus linear least squares using transformed data
โœ A. John Bailer; Christopher J. Portier ๐Ÿ“‚ Article ๐Ÿ“… 1990 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 341 KB

Drug concentrations in one-compartment systems are frequently modeled using a single exponential function. Two methods of estimation are commonly used for determining the parameters of such a model. In the first method, non-linear least-squares regression is used to calculate the parameters. In the