文献类型：期刊文献

英文题名：An adaptive neural network approach for operator functional state prediction using psychophysiological data

作者：Wang, Raofen[1];Zhang, Yu[2];Zhang, Liping[1]

机构：[1]Shanghai Univ Engn Sci, Sch Elect & Elect Engn, Shanghai 201620, Peoples R China;[2]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2016

卷号：23

期号：1

起止页码：81

外文期刊名：INTEGRATED COMPUTER-AIDED ENGINEERING

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000368211200006)】；

基金：This work was supported by National Nature Science Foundation under Grant No. 61403249 and No. 61305028, Fundamental Research Funds for the Central Universities under Grant WH1314023, and the Shanghai University Young Teachers' Training Scheme Funds ZZGJD12011. The authors would like to gratefully acknowledge Professor D Manzey, TU Berlin, Germany for providing the AUTOCAMS software which made the data collection experiments essential for this work possible.

语种：英文

外文关键词：Operator functional state; electroencephalogram; electrocardiogram; differential evolution; ant colony; neural network

摘要：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.