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Cognitive Neurodynamics

, Volume 7, Issue 5, pp 395–407 | Cite as

Predictive modeling of human operator cognitive state via sparse and robust support vector machines

  • Jian-Hua ZhangEmail author
  • Pan-Pan Qin
  • Jörg Raisch
  • Ru-Bin Wang
Research Article

Abstract

The accurate prediction of the temporal variations in human operator cognitive state (HCS) is of great practical importance in many real-world safety-critical situations. However, since the relationship between the HCS and electrophysiological responses of the operator is basically unknown, complicated and uncertain, only data-based modeling method can be employed. This paper is aimed at constructing a data-driven computationally intelligent model, based on multiple psychophysiological and performance measures, to accurately estimate the HCS in the context of a safety-critical human–machine system. The advanced least squares support vector machines (LS-SVM), whose parameters are optimized by grid search and cross-validation techniques, are adopted for the purpose of predictive modeling of the HCS. The sparse and weighted LS-SVM (WLS-SVM) were proposed by Suykens et al. to overcome the deficiency of the standard LS-SVM in lacking sparseness and robustness. This paper adopted those two improved LS-SVM algorithms to model the HCS based solely on a set of physiological and operator performance data. The results showed that the sparse LS-SVM can obtain HCS models with sparseness with almost no loss of modeling accuracy, while the WLS-SVM leads to models which are robust in case of noisy training data. Both intelligent system modeling approaches are shown to be capable of capturing the temporal fluctuation trends of the HCS because of their superior generalization performance.

Keywords

Human operator cognitive state Least squares support vector machine Sparseness Robustness Regressive model 

Notes

Acknowledgments

The authors would also like to thank Prof. D Manzey, Technical University Berlin, Germany, for providing the AUTO-CAMS software used in our OFS data acquisition experiments. The work supported by the National Natural Science Foundation of China (under Grant No. 61075070 and Key Grant No. 11232005) and a Senior Research Fellowship from the Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Jian-Hua Zhang
    • 1
    • 2
    Email author
  • Pan-Pan Qin
    • 1
  • Jörg Raisch
    • 3
    • 4
  • Ru-Bin Wang
    • 2
  1. 1.Department of AutomationEast China University of Science and TechnologyShanghaiPeople’s Republic of China
  2. 2.Institute of Cognitive NeurodynamicsEast China University of Science and TechnologyShanghaiPeople’s Republic of China
  3. 3.Control Systems GroupTechnische Universität BerlinBerlinGermany
  4. 4.Systems and Control Theory GroupMax Planck Institute for Dynamics of Complex Technical SystemsMagdeburgGermany

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