Optimization of Human Perception Modeling Using Interval Analysis
Erik-Jan Van Kampen, Peter M. T. Zaal, Elwin de Weerdt, Q. P. Chu, Jan Albert Mulder · 2008
Estimating multimodal pilot model parameters from experimental data requires solving a global nonlinear optimization problem with many local minimums. With traditional parameter estimation techniques, the solution depends on the initial parameter estimate and a local optimum can be found instead of the global optimum. In this paper, the parameter optimization is performed by using the theory of interval analysis, which describes the properties of intervals of numbers instead of crisp numbers. Interval analysis has been shown to be an excellent tool for global nonlinear optimization and it can guarantee that the global minimum of the cost function is found. The interval optimization method is applied to data from an experiment investigating the role of optic flow and the influence of physical motion cues during control of self-motion. A comparison between gradient-based and interval optimization shows that the interval method can find the global minimum of the cost function, resulting in the optimal set of model parameters, whereas gradient-based methods often converge to a local minimum.