Operator Performance Prediction based on Fuzzy Modeling Approach

Abstract

Abstract In this paper, physiological signals were measured from five participants, each participating in two sessions of experiment with identical experimental procedure. A simulation platform, AutoCAMS (Automation-enhanced Cabin Air Management System), was used to simulate a complex task environment of human-machine shared process control. Fuzzy models were constructed to quantitatively predict the human operator performance based on three EEG input features. The incremental-PID-controlled particle swarm optimization (IPID-PSO) algorithm was utilized to optimize the parameters of fuzzy models. The IPID-PSO algorithm incorporated incremental-PID-controlled search strategy to speed up the convergence of standard PSO algorithm. The operator performance modeling results are given to show the effectiveness of the IPID-PSO-tuned fuzzy modeling approach proposed to momentary operator performance assessment problem under consideration.