System identification: theory for the user
System identification: theory for the user
Fuzzy model of a human control operator in a compensatory tracking loop
International Journal of Man-Machine Studies
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A course in fuzzy systems and control
A course in fuzzy systems and control
Extracting fuzzy control rules from experimental human operatordata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An intelligent driver warning system for vehicle collision avoidance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Identification of driver state for lane-keeping tasks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Paper: Human dynamics in man-machine systems
Automatica (Journal of IFAC)
Brief A fixed-order optimal control model of human operator response
Automatica (Journal of IFAC)
Dynamics of driver vehicle steering control
Automatica (Journal of IFAC)
An optimal control model of human response part I: Theory and validation
Automatica (Journal of IFAC)
A study on the effect of particle size on coal flotation kinetics using fuzzy logic
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Survey paper: A survey on industrial applications of fuzzy control
Computers in Industry
Hybrid recommendation approaches for multi-criteria collaborative filtering
Expert Systems with Applications: An International Journal
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Driving a car and piloting an 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 an intelligent modeling 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 human operators. ANFIS, having only 32 rules, provided much better prediction results than ARX model.