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Neural network models of human operator performance

Published online by Cambridge University Press:  04 July 2016

A. May
A. May*
Affiliation:
Centre for Defence Analysis Farnborough, UK
*
†Currently at the Directorate of Science (Air), MoD, London, UK
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Abstract

This paper examines the feasibility of using neural networks to represent the effects of human operators in computer models of complex man-machine systems. In the suggested approach, data from man-in-the-loop simulators are used to train the networks. The method has been tested on several typical data sets using a stand-alone prototype system, consisting of a three-layer feed-forward network with a Chemotaxis training algorithm. Successful results have been obtained, and these can be used to place constraints on the quality, quantity and type of simulator data required in future applications.

Type
Research Article
Information
The Aeronautical Journal , Volume 101 , Issue 1004 , April 1997 , pp. 155 - 158
Copyright
Copyright © Royal Aeronautical Society 1997 

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References

1. Wohl, J.G. Force management decision requirements for air force tactical command and control, IEEE Trans SMC-11, 1981, p 618.Google Scholar
2. Dockery, J.T. and Woodcock, A.E.R. The Military Landscape: Mathematical Models of Combat, Woodhead Publishing, 1993.Google Scholar
3. Porter, D.B. Computer games and cognitive processes: two tasks, two modes, too much? Brit J Psych, 1991,82, p 343.Google Scholar
4. Dreyfus, H.L. and Dreyfus, S.E. Making a Mind versus Modelling the Brain. In: The AI Debate, Graubard, S.R. (Ed), MIT Press, 1988, p 15.Google Scholar
5. Weiss, S.M. and Kulikowski, C.A. Computer Systems that Learn, Morgan Kaufmann, 1991.Google Scholar
6. Ripley, B.D. Statistical Aspects of Neural Networks, Department of Statistics, University of Oxford, 1992.Google Scholar
7. Montgomery, G.J. and Drake, K.C. Abductive networks. In Applications of Artificial Neural Networks, SPIE, 1990,1294, p 56.Google Scholar
8. Wasserman, D. Neural Computing: Theory and Practice, Van Nostrand, 1989.Google Scholar
9. Hertz, J., Krogh, A. and Palmer, R.G Introduction to the Theory of Neural Computation, Addison-Wesley, 1991.Google Scholar
10. Bremmermann, H.J. and Anderson, R.W. An Alternative to Back Propagation, Technical Report PAM-483 (University of California, Berkeley), 1990.Google Scholar
11. Bushnell, M.J., Bishop, J.M. and Westland, S. A comparison between Chemotaxis and back propagation learning. International Conference on Neural Networks, Brighton, March 1992.Google Scholar
12. Baba, N. A new approach for finding the global minimum of error functions of neural networks, Neural Networks 2, 1989, p 367.Google Scholar
13. Fix, E. and Armstrong, H.G. Neural network based human performance modelling, IEEE NAECON 1990,3, p 1162.Google Scholar