Operator Performance Prediction based on Fuzzy Modeling Approach

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.