Affiliations: [a] School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China | [b] Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai, China
Correspondence:
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Corresponding author: Raofen Wang, School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Room 915, Administration Building, 333 Rd. Longteng, Shanghai 201620, China. Tel./Fax: +86 021 67791126; E-mail:[email protected]
Abstract: In highly automated human-machine systems, human operator
functional state (OFS) prediction is an important approach to prevent
accidents caused by operator fatigue, high mental workload, over anxiety,
etc. In this paper, psychophysiological indices, i.e. heart rate, heart rate
variability, task load index and engagement index recorded from operators
who execute process control tasks are selected for OFS prediction. An
adaptive differential evolution based neural network (ACADE-NN) is
investigated. The behavior of ant colony foraging is introduced to
self-adapt the control parameters of DE along with the mutation strategy at
different evolution phases. The performance of ACADE is verified in the
benchmark function tests. The designed ACADE-NN prediction model is used for
estimation of the operator functional state. The empirical results
illustrate that the proposed adaptive model is effective for most of the
operators. The model outperforms the compared modeling methods and yields
good generalization comparatively. It can describe the relationship between
psychophysiological variables and OFS. It's applicable to assess the
operator functional state in safety-critical applications.
