文献类型：期刊文献

英文题名：Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets

作者：Zhang, Jian-Hua[1];Xia, Jia-Jun[1];Garibaldi, Jonathan M.[2];Groumpos, Petros P.[3];Wang, Ru-Bin[4]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]Univ Nottingham, Sch Comp Sci, Nottingham NG8 1BB, England;[3]Univ Patras, Dept Elect & Comp Engn, Patras 26500, Greece;[4]East China Univ Sci & Technol, Inst Cognit Neurodynam, Shanghai 200237, Peoples R China

年份：2017

卷号：144

起止页码：147

外文期刊名：COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

收录：;EI(收录号：20171403540060);WOS:【SCI-EXPANDED(收录号:WOS:000402214800014)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant No. 61,075,070 and Key Grant No. 11,232,005.

语种：英文

外文关键词：Man-machine system; Fuzzy inference petri net; Operator functional state; Human performance; Adaptive functional allocation; Electroencephalography

摘要：Background and objective: In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task load (control) variable and continuous operator performance (system output) variable. Methods: Petri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroen-cephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. Results: Experiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). Conclusion: The simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller. (C) 2017 Elsevier B.V. All rights reserved.