Modeling of Human Operator Performance Utilizing Time Series Analysis

Abstract

A new method for modeling the human operator from actual input-output data utilizing time series analysis is discussed in this applications oriented paper. The technique first identifies the form of the model, then estimates the parameters of the identified model based on actual data. Finally it checks the fitted model in relation to the data with the aim of revealing model inadequacies, thus providing model improvement. The methodology for applying the time series technique for determining the model of the human element in a feedback system is discussed. In addition, an approach for determining the human model under various levels of stress is discussed. The time series approach is a useful method for modeling any set of discrete observables corrupted with noise, be it human or some other deterministic/stochastic process. Since this is the first time that the human model has ever been obtained from the time series method, it is quite understandable that the results described shed new light on certain aspects of this problem, reveal new insights into the human model, and ask other probing questions.