Adaptive Fuzzy Approaches to Modelling Operator Functional States in a Human-Machine Process Control System

Author

Mahfouf, M. ; Zhang, J. ; Linkens, D.A. ; Nassef, A. ; Nickel, P. ; Hockey, G.R.J. ; Roberts, A.C.

Author_Institution

Sheffield Univ., Sheffield

fYear

2007

fDate

23-26 July 2007

Firstpage

1

Lastpage

6

Abstract

This paper assesses the operator functional state (OFS) of human operators based on a collection of psychophysiological and performance measures. Two types of adaptive fuzzy models, 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 adaptive fuzzy modelling procedures are described and then validated using real-life data measured from such a simulated human-machine process control system.

Keywords

fuzzy reasoning; fuzzy set theory; genetic algorithms; human computer interaction; physiological models; psychology; Mamdani fuzzy model; adaptive fuzzy model; adaptive-network-based fuzzy inference system; automation-enhanced cabin air management system; genetic algorithm; human operator; human-machine process control system; operator functional state; psychophysiological measures; simulated process control; Adaptive control; Fuzzy control; Fuzzy sets; Fuzzy systems; Genetic algorithms; Humans; Man machine systems; Process control; Programmable control; Psychology;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International

Conference_Location

London

ISSN

1098-7584

Print_ISBN

1-4244-1209-9

Electronic_ISBN

1098-7584

Type

conf

DOI

10.1109/FUZZY.2007.4295371

Filename

4295371