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Adaptive fuzzy systems for target tracking

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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 )

Date of Publication:

Autumn 1992

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