Computing Linear and Nonlinear Normal Modes under Interval (and Fuzzy) Uncertainty

For linear systems, it is often possible to combine the original variables into linear combinations ("normal modes") that evolve independently on each other. The transformation to such normal models makes the analysis of the corresponding systems much easier. In this paper, we analyze how this easiness can be extended to nonlinear systems. We also analyze how the results of this analysis are affected by interval and fuzzy uncertainty