Adaptive fuzzy systems for target tracking

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