Operator Functional State Assessment Based On Structure Optimized Evolutionary Fuzzy System

Operator Functional State (OFS) is an important factor that affect system security in complex human-machine system. This paper adopts Fuzzy Cooperative Co-evolutionary (FCC) modeling algorithm to establish OFS models based on a series of electrophysiological signals and operator performance data. This method encode the main parameters of fuzzy model in different populations, such as the antecedent/post of fuzzy rules, membership function parameters, to optimize model structure and parameters synchronously. The individual fitness function in algorithm populations consider both of model accuracy and interpretability by using a components weighted-sum method which transit multi-objective optimization into single-objective optimization. FCC method is used to establish fuzzy classification systems for five UCI data sets before OFS modeling. And the simulation results verified its effectiveness. According to the sensitivities of EEG and ECG markers of different operators are different, a Simba method is adapted to select input variables. The simulation results show that each OFS fuzzy model has good accuracy and simplified structure, evolves less rules and fuzzy sets. Comparison to GA fuzzy modeling method, FCC has better generalization performance for establishing OFS model. The final model based on the results is used to adjust control strategies, achieving intelligent human-computer interaction.