By Topic

Adaptive fuzzy systems for target tracking

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 $31
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)
Pacini, P.J. ; Signal & Image Process Inst., Univ. of Southern California, Los Angeles, CA, USA ; Kosko, B.

Compares fuzzy and Kalman-filter control systems for real-time target tracking. Both systems performed well in the presence of additive measurement noise. In the presence of mild process (unmodelled-effects) noise, the fuzzy system exhibited finer control. The authors tested the robustness of the fuzzy controller by removing random subsets of fuzzy associations or `rules', and by adding destructive or `sabotage' fuzzy rules to the fuzzy system. They tested the robustness of the Kalman tracking system by increasing the variance of the unmodelled-effects noise process. The fuzzy controller performed well until over 50% of the fuzzy rules were removed. The Kalman controller's performance quickly depreciated as the unmodelled-effects variance increased. The authors used unsupervised neural-network learning to adaptively generate the fuzzy controller's fuzzy-associative-memory structure. The fuzzy systems did not require a mathematical model of how system outputs depended on inputs

Published in:

Intelligent Systems Engineering  (Volume:1 ,  Issue: 1 )