By Topic

Scaling of the sampling period in nonlinear system identification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
T. Wigren ; Dept. of Inf. Technol., Uppsala Univ., Sweden

The paper presents a scaling algorithm for system identification, based on a nonlinear black box differential equation model. The model is discretized by an Euler forward numerical integration scheme. A scale factor is applied to the explicitly appearing sampling period, when iterating the discrete time state space model and the corresponding gradient recursion. The result is an exponential scaling of the state components of the model, and a scaling of the estimated parameter vector. The original parameter vector can be explicitly calculated from the scaled parameter vector using a diagonal matrix that is a function only of the scale factor. A new analysis of the effect of scaling on the Hessian, shows how the same diagonal matrix affects its eigenvalue distribution. A simulation study illustrates the beneficial effects on e.g. the condition number that can be obtained with the algorithm.

Published in:

Proceedings of the 2005, American Control Conference, 2005.

Date of Conference:

8-10 June 2005