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Published online by Cambridge University Press: 04 July 2016
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.