By Topic

Decision rules for distributed decision networks with uncertainties

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
$31 $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

2 Author(s)
Drakopoulos, E. ; Dept. of Electr. Eng. & Comput. Sci., Northwestern Univ., Evanston, IL, USA ; Chung-Chieh Lee

The authors study distributed decision networks where uncertainties exist in the statistical environment. Specifically each decision maker (DM) has an unknown probability to be jammed or defective and an unknown probability to provide an incorrect decision when jammed or defective. Each DM in the network has the ability to process its input data consisting of external observations and decisions from preceding DMs, to produce a decision regarding an underlying binary hypothesis testing problem. The local observations are assumed conditionally independent given each hypothesis. The resulting binary hypothesis testing problem is solved using some simple concepts of Dempster-Shafer theory. The performance of the proposed decision rule is compared to that of the minimax decision rule and the decision rule that is optimum when there are no jammed or defective DMs for several distributed decision networks with different topologies. It is shown that the proposed decision rule has a very robust behavior

Published in:

Automatic Control, IEEE Transactions on  (Volume:37 ,  Issue: 1 )