Adaptive Fuzzy Model of Operator Functional State in Human-Machine System: A Preliminary Study

J. Zhang, M. Mahfouf, D.A. Linkens, P. Nickel, G.R.J. Hockey, and A.C. Roberts (UK)


OFS, fuzzy system, genetic algorithm


This paper assesses the operator functional state (OFS) based on a collection of psychophysiological (i.e., cardiovascular and EEG) and performance measures. Two types of adaptive fuzzy model, namely ANFIS (adaptive network-based fuzzy inference system) and GA (genetic algorithm) based Mamdani fuzzy model, are employed to estimate the OFSs under a set of simulated process control tasks involved in an automation-enhanced cabin air management system (aCAMS). The fuzzy modelling procedures are described in detail. The adaptive fuzzy models are validated using real-life data measured from two well-trained collegiate participants. The preliminary simulation results implied that the overall performance of human-machine system may be improved by identifying and predicting OFSs via the proposed fuzzy models.

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