Optimization of Human Perception Modeling Using Interval Analysis

Abstract

Estimating multi-modal pilot model parameters from experiment or simulation data requires solving a global nonlinear optimization problem with many local minimums. Using the traditional parameter estimation techniques, finding the global optimum is dependent on the initial parameter estimate. In this paper the parameter optimization is performed by using the theory of interval analysis, which deals with 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, while the gradient-based method often converges to a local minimum.