By Topic

Convergence analysis techniques: Comparison and contrast

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)
John R. Treichler ; ARGO Systems, Inc., Sunnyvale, CA

The convergence behavior of an adaptive processor is usually a very important aspect of the system's performance and in fact many processor parameters are usually chosen with the goal of optimizing, or at least manipulating, the convergence rate. In spite of this common interest, several methodologies for analyzing convergence behavior have been developed, principally because different applications often require different behavioral knowledge and because no single technique provides all the answers. The purpose of this paper is to compare and contrast convergence analysis techniques used in the fields of adaptive filtering, adaptive identification, and adaptive control. The methods explored include both nonlinear stability analysis and stochastic analysis. Particular attention is paid to the underlying assumptions and useful outputs for each approach.

Published in:

Decision and Control including the Symposium on Adaptive Processes, 1980 19th IEEE Conference on

Date of Conference:

10-12 Dec. 1980