Operator functional state (OFS) is defined as the time-variable ability that an operator completes his/her assigned tasks. To evaluate the OFS in safety-critical human-machine systems, it is modeled by using the Wang-Mendel-based fuzzy system paradigm in this paper. The fuzzy model is constructed to correlate three EEG features (as model inputs) to the human-machine system performance (as model output). To derive a fuzzy model for real-time OFS assessment, the Gaussian membership function membership crossover point membership gradeδis found to be an essential parameter that controls the robustness of data-driven fuzzy models. The fuzzy models with differentδare applied to the OFS fuzzy modeling. The results have demonstrated that an appropriate value ofδcan be selected to derive robust fuzzy models. Compare with the results obtained by fuzzy models based on symmetric Gaussian membership functions, the new approach based on asymmetric Gaussian membership function leads to considerably improved robustness performance.
SYSTEMATIC LITERATURE REVIEW ON THE APPLICATION OF DATA MINING METHODS TO MONITOR THE OPERATOR FUNCTIONAL STATE IN HUMAN-MACHINE SYSTEMS
