By Topic

Application-level robustness and redundancy in linear systems

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)
Alippi, C. ; Dipt. di Elettronica e Inf., Politecnico di Milano, Italy

The paper quantifies the degradation in performance of a linear model induced by perturbations affecting its identified parameters. We extend sensitivity analyses available in the literature, by considering a generalization-based figure of merit instead of the inaccurate training one. Effective off-line techniques reducing the impact of perturbations on generalization performance are introduced to improve the robustness of the model. It is shown that further robustness can be achieved by optimally redistributing the information content of the given model over topologically more complex linear models of neural network type. Despite the additional robustness achievable, it is shown that the price we have to pay might be too high and the additional resources would be better used to implement a n-ary modular redundancy scheme

Published in:

Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on  (Volume:49 ,  Issue: 7 )