Predictive modeling of human operators using parametric and neuro-fuzzy models by means of computer-based identification experiment

Ertugrul S.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, vol.21, no.2, pp.259-268, 2008 (SCI-Expanded) identifier identifier


Driving a car and piloting in airplane are the most common examples for manual control of complicated processes. Human operators are known to be nonlinear, adaptive, time varying and intelligent controllers. In some cases, the human operator may or may not be well trained or an expert, showing different dynamics from operator to operator as in driving example. Therefore, it is very difficult to obtain mathematical models of human operators in a human- in-the-loop-manual control tasks. The goal of this research is to find a simple dynamic model for the prediction of the human Operator actions in a manual control system. A computer-based experiment has been designed using the system identification theory to collect data from human operators. The autoregressive with exogenous inputs (ARX), as a parametric model and the adaptive-network-based fuzzy inference system (ANFIS), as in intelligent modelling approach that has the advantages of both neural networks and fuzzy logic, have been investigated and compared for simple and fast implementation to predict the response of hull-fall Operators. ANFIS, having only 32 rules, provided much better prediction results than ARX model. (c) 2007 Elsevier Ltd. All rights reserved.